Effective Rates Collection Via GIS Application. The Case of Lusaka City Council, Zambia

TABLE OF CONTENTS

CERTIFICATION OF APPROVAL

ABSTRACT

DEDICATION

ACKNOWLEDGEMENTS

LIST OF FIGURES

LIST OF TABLES

LIST OF APPENDICES

ACRONYMS

CHAPTER ONE: BACKGROUND TO THE STUDY
1.1 Introduction
1.2 Background
1.3 Problem Statement
1.4 Research Questions
1.5 Aim
1.6 Objectives of the Research
1.7 Scope of the Study
1.8 Significance of the Study
1.9 Organization of the Dissertation

CHAPTER TWO: LITERATURE REVIEW
2.1 Geographic Information Systems
2.2 Components of a Geographic Information System
2.2.1 Hardware
2.2.2 Software
2.2.3 Data
2.2.4 People
2.2.5 Methods
2.3 Functions of GIS
2.3.1 Input
2.3.2 Manipulation
2.3.3 Management
2.3.4 Query
2.3.5 Analysis
2.3.6 Visualization
2.4 ArcGIS
2.4.1 ArcGIS Desktop
2.4.2 Server GIS
2.4.3 Developer GIS
2.4.4 Mobile GIS
2.5 GIS in Developing Countries
2.5.1 GIS in Zambia
2.5.2 GIS for Local Authorities
2.6 Property Rates/Property Tax
2.6.1 Property Tax Mapping
2.7 Empirical Research: Design and Application of Similar Studies
2.7.1 Application of GIS in Improving Tax Revenue from the Informal Sector in Bayelsa State, Nigeria
2.7.2 Enhancing revenue collection through integrated GIS database system with property (land) Classification using an algorithm
2.7.3 Database Creation for Tenement Rate Collection: The Role of GIS
2.8 Summary

CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Research Design
3.2 Needs Assessment
3.3 Data Collection
3.4 Data development and conversion
3.5 Database System development: Database design
3.5.1 Database System development: Desktop GIS and MySQL
3.6 System Design and Implementation
3.7 Existing System
3.8 Image Data
3.9 Vector Data
3.10 Non-Spatial data
3.11 GIS System Design
3.12 Geodatabase design
3.13 MySQL Database
3.13.1 PropertyPrimary Table
3.13.2 Ownership Land Use
3.13.3 Valuation
3.13.4 Tbl History
3.13.5 User Table
3.14 Graphical User Interface
3.15 DesktopGIS
3.16 Limitations
3.17 Summary

CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 GIS Based Rates Collection System
4.2 Colour-Coded Map
4.2.1 Land use analysis map
4.2.2 Rateable Value analysis map
4.2.3 Status analysis map
4.3 ArcReader Graphical User Interface
4.4 Discussion of Results

CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
5.2 Conclusion
5.3 Recommendations

REFERENCES

APPENDICES

ABSTRACT

Property tax is one of the most important sources of revenue for the Lusaka City Council and accounts for nearly one third of its annual revenues. This source of revenue is the least tapped but contributes significant fiscal support to urban governments in Africa and the rest of the world. Rates are in essence a local tax paid by occupiers of land and buildings as a contribution towards the provision of services. One of the major topics of discussion in Zambia has been the issue of decentralization which would entail more responsibilities placed on the local authorities. This added responsibility requires effective means of raising funds and one major source is through property taxes. Since rates are payable on immovable property it is impossible to avoid paying them and therefore local authorities have a stable form of income.

Lusaka City Council has major challenges in effectively collecting property taxes because there the organisation has adopted manual methods of rates collection instead of an integrated system connecting the available maps and the information contained in the valuation roll (non-spatial). This greatly affects the efficiency of the revenue collection process thereby affecting the total amount budgeted for from collections. Having an electronically managed tax database (spatial data) would be advantageous as many studies have indicated that the efficiency of tax collection can be improved and a higher degree of transparency achieved.

The aim of this research is to develop a GIS based Tax Information System that would improve the effectiveness of collecting property or ground rates by enabling spatial query, visualization, efficient updating and processing of property tax records. The strength of a GIS based Information system developed lies in the capability to incorporate all the spatial and non-spatial property tax information.

The results of the study show that information in the valuation roll can be placed in the relational database and linked to a Geodatabase in ArcGIS where analysis can take place and various commands and queries (SQL) performed. This entails informed decision making as regards to the management of rates collection.

Keywords: GIS, ArcGIS, SQL, Spatial, Non-spatial.

DEDICATION

As I navigate through the terrain of academic enlightenment, it all wouldn’t have been possible without the constant support of my parents. This dissertation is dedicated to them; Watson and Patricia Kaputula. Truly education is freedom!

ACKNOWLEDGEMENTS

The following persons and organisations have been key in the drafting of this dissertation:

The guidance and passion of Dr. Faustin Banda, the Late Dr. Augustine Mulolwa, Mr. Ernest Munsanje for his insight and comments.

Grateful acknowledgement is made to the office of the director of the Department City Planning and the Department of Valuation and Real Estate at Lusaka City Council.

To my fellow postgraduate students and classmates. Let’s soar higher and higher!

Last but not the least to my family and circle of trust. Your love and encouragement have brought me this far.

LIST OF FIGURES

Figure 1: Lusaka City boundary

Figure 2: GIS components

Figure 3: GIS Key Functions

Figure 4: ArcGIS Applications

Figure 5: Methodology flowchart

Figure 6: LCC Rate Bill

Figure 7: Study area boundary overlaid on a satellite image

Figure 8: Study area boundary overlaid on the City topographic map

Figure 9: Map showing properties and streets

Figure 10: Extract of LCC valuation roll

Figure 11: MySQL workbench interface

Figure 12: User name and password access into the MySQL database

Figure 13: Database Creation in MySQL

Figure 14: Property primary database table

Figure 15: Ownership Land use database table

Figure 16: Valuation database table

Figure 17: History database table

Figure 18: User table database table

Figure 19: Entity Relationship Diagram

Figure 20: ODBC database connection

Figure 21: Linkage to created tables

Figure 22: Addition of database connection

Figure 23: Database tables in ArcGIS

Figure 24: Joining of database objects in ArcMap

Figure 25: ArcReader 2.1 user interface

Figure 26: Link from databases to the GUI

Figure 27: SQL search for commercial properties and visualisation

Figure 28: Number of properties vs Use code chart based on the valuation information from study area.

Figure 29: Statistical analysis for commercial properties in study area

Figure 30: Statistical distribution of rates in study area for residential properties

Figure 31: Statistical distribution of rates in study area for institutional properties

Figure 32: Number of properties with the lowest rate charges per year

Figure 33: Number of properties with the highest rate charges per year

Figure 34: Land use colour-coded map for the study area.

Figure 35: Colour-code map based on rateable values of parcels

Figure 36: Colour-code map based on the status of bill distribution

Figure 37: The identify tool (circled red) used to view all attributes attached to a particular property.

LIST OF TABLES

Table 1: GIS vs Traditional Tasks

Table 2: Free or Open source GIS software

Table 3: Common commercial GIS software

Table 4: Commercial spatial database management systems

Table 5: Pros and cons of FOSS and Commercial software

Table 6: Review of case studies

Table 7: Area codes for Rates collection

Table 8: Image Data

Table 9: Feature dataset and feature classes of the study area

Table 10: Link between geodatabase and relational database

Table 11: Digitisation feature results

Table 12: Table showing the land uses in Study area using ArcGIS

LIST OF APPENDICES

APPENDIX A: ANCILLARY DATA

Appendix A 1: Semi-structured interview guide

Appendix A 2: Data Description

APPENDIX B: MAPS

Appendix B 1: Hard copy maps used at LCC and storage drawers

Appendix B 2: Parcel-based Study area Map without Improvement Footprints

Appendix B 3: Parcel-based Study area map with Improvement Footprints

ACRONYMS

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CHAPTER ONE: BACKGROUND TO THE STUDY

1.1 Introduction

This chapter highlights the background on the need for Lusaka City Council to adopt a GIS based system approach in the collection of ground rates. The research suggests the use of Desktop GIS platforms that respond to the needs identified in the problem statement. It further outlines the research questions and objectives. The chapter finally sets the scope for the study and underscores the significance of the study by providing the benefits to be derived.

Local governments use Geographic Information Systems (hereafter called GIS) to improve decision making, service delivery, and citizen engagement (ESRI, 2016). Since most government data is location based, equipping your organization with the latest in GIS technology provides the tools needed to make your community better. Operations run more smoothly, and citizens have more positive experiences. According to Worrall (1994) there is been a growing interest amongst municipalities with regards to the use of GIS in daily operations.

According to Gupta et.al (2001) the effectiveness of GIS is dependent on how well it has been integrated within the operation of the municipality as a whole. The use of GIS technology should be integrated within the overall strategy of the municipality by using GIS as a vehicle to achieve its objectives and goals. Government and municipalities utilize such technology to plan, maintain and manage their data. GIS databases assist in the integration of many types of data; specifically, geographic data that are available in different types, formats, locations, and sizes (Buckley, 1998).

GIS can be applied as a significant technology when it comes to assisting in managing the planning and maintenance of infrastructure within various municipalities. The proliferation testifies to the optimistic belief in this new technology’s capabilities and prospective benefits. The growing urban population (worldwide), increasing at a much faster rate than the population as a whole (Worrall, 1994), has resulted in the promotion of sustainable development and hence led to an increased pressure on the urban environment. These changes in the population have a profound effect on the demand for services, not only in the city councils but just as importantly in the district and local municipalities in Zambia. According to Mutale (2011) GIS technology

has developed to such an extent that it has penetrated a number of academic subjects and has integrated further into mainstream business. There has been a gradual increase in human activity dependency on GIS and Geographic Information (Coyne et at, 2000). GIS application in our everyday life has also increased as is evidenced by its application in the many services we rely on. Researchers, scientists and administrators are increasingly using GIS as a decision­making tool to inform about real world scenarios.

GIS has the capabilities to provide necessary physical input and intelligence for preparation of base-maps, formulation of planning proposals and to act as monitoring tool during implementation phase of any planning scheme (Gupta et al, 2001).

GIS tools help planners analyse problems more quickly and thoroughly and formulate solutions to monitor progress for achieving long-term goals for the community development. GIS plays an extremely important role in resource management and land use planning activities. By integrating and organizing information spatially, revenue planners can get a broad view of the current situation and more accurately assess the future scenarios thereby encouraging sustainable development.

1.2 Background

Lusaka is the capital city of Zambia, it is 1280 meters above sea level with the city covering an area of 375 km2 of mostly flat relief (UN-HABITAT, 2007). It dominates the country’s urban system and accounts for 32 percent of the total urban population in the country.

Planning for Lusaka has been inadequate due to insufficient resources at the Lusaka City Council (UN-HABITAT, 2007). The major problems in the city include the lack of serviced land, speculation on land, complex procedures and poor record keeping regarding land ownership and land use, inadequate human resources, the slow pace in issuing security of land tenure, the failure of master planning, an increase in illegal settlements, and political interference in land allocation (UN-HABITAT, 2007).

Lusaka’s central location, in addition to its capital city status, gives it strategic importance, as it is easily accessible from all parts of the country. Lusaka is experiencing typical urban problems associated with developments such as population growth, high levels of urbanization, unemployment, a lack of services, and inadequate waste management. In order to meet the demand for service delivery, systems and procedures that assist in providing the required resources and management in a more efficient and effective manner need to be developed. Figure 1 shows the location of the City of Lusaka.

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Source: (LCC, 2017)

The revenue collected by LCC enables them to execute developmental projects that as a result improve the standard of living of its citizens as well as meet its recurrent expenditure. It is therefore expedient that city council adopt suitable revenue collection models that will expand their internally generated revenue to enable effective service delivery.

Around the world, local governments use GIS to improve decision making, service delivery, and citizen engagement (ESRI, 2016). Since most city council data is location based, equipping the organization with the latest in GIS technology provides them with the tools needed to make your communities better, operations run more smoothly, and citizens have more positive experiences (ESRI, 2016).

LCC has been moving away from the traditional hard copy maps to soft copy maps in various formats such as Scanned hardcopies, CAD format and GIS shapefiles. This shift has been helpful in the process of updating and reproducing of the city maps and layouts. The major challenge is that there is no GIS system that is directly related to the planning and collection of ground rates. The current method being used by LCC of using hard copy maps in planning for ground rates proves to be a challenge as hardcopies do not provide the dynamism that is provided by softcopy maps. LCC needs a system that is more efficient in spatial data organisation, manipulation, and visualisation and can be linked to other databases.

Mahaxay et.al (2012) describe a Geographic Information System as a system that captures, stores, analyses, manages and presents (digitized) data that is linked to one or more locations on a map. Unlike a flat paper map, where what you see is what you get, GIS can present many layers of different information (Mahaxay et.al, 2012).In order to effectively carry out service delivery, a city council requires current, accurate, and relevant land data and its associated information integration to meet the present problems of city management. What distinguishes GIS from CAD and other mapping formats is the capability to connect to other information systems or databases and make it valuable to a wide range of public and private enterprises for explaining events, predicting outcomes, and planning strategies.

1.3 Problem Statement

The Lusaka City Council uses a system of rates collection which is based on manual method and is inefficient, time-consuming and prone to error and abuse.

This process includes collection of hardcopy maps by mail-runners (Bill distribution officers). The bills have a section the indicates the property number and a forerunner having been given a number of bills collects a hardcopy map from the Survey section of LCC and relates the plot numbers on the bills to those on the specific area of map where the bill must be delivered. This process is cumbersome as the paper maps can only be unfolded to view the information on the map and are easily subject to wear and tear. Also this method does not allow for intelligent analysis of the process of bill distribution and the subsequent rates collection.

The lack of graphical and mapped visualisation of where bills have been distributed and were payments have been made makes it difficult also to keep accountability. This entire procedure neglects any digital processes and this becomes difficult to plan for the distribution activities and in-turn results in the loss of revenue for LCC and failure to provide services for the communities.

1.4 Research Questions

1. Are there Geographic Information Systems that guide on ground rates collection?
2. Can GIS based system improve property tax collection and tracking of revenue?
3. Can dynamic maps developed in GIS improve rate collection?

1.5 Aim

To apply GIS for the effective collection of ground rates at City Council level.

1.6 Objectives of the Research

1. Assess the current use of GIS in ground rates collection.
2. Create a GIS driven database for effective collection of rates on properties in study area.
3. Analyse the variations in rateable value statuses on properties and produce a colour coded map displaying the variations.

1.7 Scope of the Study

This study was limited to developing a GIS based rates collection tool for Lusaka City Council and the area selected in the City of Lusaka. The tools used to develop the tool/system were free open source MySQL to develop the relational database and ArcGIS (a commercially available software package developed by ESRI) to create spatial data. This was important as no algorithm or coding was required in the development of the GIS based tool. ArcReader, which is also an ESRI based application was used as a Graphical User Interface (GUI) for the end user.

1.8 Significance of the Study

The use of a GIS based system in collecting rates will provide the stuff at LCC a tool that enables visualisation of the process enabling proper planning, analysis and decision making before, during and after the process of rates collection. The research intends to bring to light the advantage of using a GIS system for the rates managers as compared to manual methods and the dynamic approach GIS offers.

1.9 Organization of the Dissertation

Chapter 1: Looks the background to the problem and suggests a GIS based rates collection system to assist in the collection of rates at LCC. The chapter finally establishes the scope and points out the significance of the study by defining the resulting benefits.
Chapter 2: This chapter presents literature on the modern concepts and approaches for DesktopGIS based rates collection system and concisely describes similar studies.
Chapter 3: This chapter looks the research design and methodology that was employed to carry out the research study and subsequent limitations. It finally presents the system design and implementation of the GIS based rates collection system based on the existing methods, techniques, tools and needs assessment.
Chapter 4: This chapter presents the findings from the system design and implementation, consequent analysis and presentation of the system features, workflows, evaluation and testing.
Chapter 5: This chapter presents the conclusion and recommendations based on the findings, analysis and presentation of the results and makes recommendation for further research.

CHAPTER TWO: LITERATURE REVIEW

2.1 Geographic Information Systems

According to the Environmental and Scientific Research Institute (ESRI, 2017), a Geographic Information system (GIS) is a framework for gathering, managing, and analysing data. Rooted in the science of geography, GIS integrates many types of data. The core of the system deals with analysing spatial locations and organizing layers of information into visualisations using maps and 3D scenes. With this unique capability, GIS reveals deeper insights into data, such as patterns, relationships, and situations- helping users make smarter decisions (ESRI, 2017). Users may range from researchers, policy-makers, and technocrats.

What distinguishes a GIS from other information systems is the capability it provides to link spatial and non-spatial data. This capability is critical in rates collection as it provides a visual aspect of the clients’ physical location apart from the monies that are due to the government. Another stand-out strength of GIS is its ability to create distinct map layers for different types of information, and then to combine them in any way desired or needed. Each layer represents a particular theme or feature of the map. These themes can be laid on top of one another, creating a stack of information about the same geographic area (UNESCO, 2012). Table 1 shows GIS strengths in relation to the traditional land use planning process.

Table 1: GIS vs Traditional Tasks

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Source: Belward and Valenzuela, 1991. P.492

2.2 Components of a Geographic Information System

In order for a GIS to work effectively, its key components need to be properly managed so as to seamlessly run through an organizations’ operations. According to Tomlinson (2013), GIS is a complex system of interconnected parts. These parts comprise of information products, data, software, hardware, procedures, and people. These parts collectively form what is called the Systems view of GIS as acknowledged by (NLS, 2007). The Systems View incorporates the Functional, Geoprocessing, and Database views of GIS, and recognizes the importance of computer hardware, software, and spatial data. In the context of rates collection this entails bringing all these components together and the current business procedures carried out in the entire process of rates collection by LCC. This research focused on the use of vendor based desktop GIS software known as ArcGIS.

A desktop GIS is a mapping software that is installed onto and runs on a personal computer and allows users to display, query, update, and analyse data about geographic locations and the information linked to those locations. (ESRI, 2008a). Desktop GIS represents the real world on a computer similar to the way maps represent the world on paper. Both desktop GIS and paper maps convey information about places and the anything spatially related to those locations. However, desktop GIS has power and flexibility that paper maps lack. The scale of the map influences the size of what appears on it. With GIS, however, you can store and link huge amounts of information about the objects represented on maps. These objects are called features.

Each map feature has a location, a representative shape, and a symbol that represents one or more of its characteristics. Because features on maps are organized according to relative position, maps are good for showing the relationships among feature locations. These relationships, called spatial relationships, are important because understanding them helps us solve problems (ESRI, 2008a).

The components of GIS are summarized in the Figure 2.

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Figure 2: GIS components

Source:http://www.esri.com/library/whitepapers/pdfs/healthserv.pdfA

2.2.1 Hardware

Hardware includes the computer on which a GIS operates, the monitor on which results are displayed, and a printer for making hard copies of the results. Today, GIS software runs on a wide range of hardware types, from centralized computer servers to desktop computers used in stand-alone or networked configurations. Tomlinson (2013), states that GIS demands hardware and it is cardinal to look at an organisations computational resources and upgrade accordingly to support a GIS system. Some hardware required for a GIS includes personnel computers, high performance workstations, Scanners, Plotters, and Printers. Also data capturing devices such as handheld devices and Global Navigation Satellite Systems equipment are important hardware requirements.

With the advent of e-government in majority of developing countries, UNESCO (2005) notes that Computing Infrastructure is another important dimension of National e-Government Infrastructure. While on one end, government needs large computing infrastructure to develop and deliver e-government services on continuous basis, infrastructure is also needed at the end of citizens to derive the benefits of these services.

Government should also consider, making their services accessible from various other media/devices such as basic telephones, mobiles, cable TV network, PDAs and many other handheld devices (UNESCO, 2005).

These are key components to GIS hardware requirements as GIS has made significant shifts from desktop GIS to embracing more portable devices.

2.2.2 Software

Software programs provide the functions needed to perform analysis and create the information products required (Tomlinson, 2013).These components include tools for the input and manipulation of geographic information, a database management system (DBMS), tools that support geographic query, analysis, and visualization, and a graphical user interface (GUI) for easy access to tools. Analytical capabilities of GIS are especially key in decision making as Demers (2000) notes that GIS analytical capabilities and its ability to generate new information from the combination and manipulation of existing data make it a powerful tool compared to other related systems. There are two common forms of GIS software available today namely proprietary software and open source. GIS software has traditionally been commercially produced, but there has been a shift with a very strong Open Source movement has developed. Commercial GIS software remains dominant in the industry today, because it has a multi­decade lead in developing technology, and because the commercial model allows for sustained development of challenging computer algorithms over a period of years. Commercial GIS remains the most common choice for large GIS projects, because the software is well developed and generally reliable (NLS, 2007).

2.2.2.1 Open Source desktop GIS software

The trend of Free/Open Source Software (FOSS) development, especially in the field of Geospatial Information Systems (GIS), has grown rapidly in recent years (Confino and Laplante, 2010). According to Confino and Laplante (2010), open source software is widely used by government, businesses, and non-profits alike because of the financial benefits. This growth in the field has enabled the use of FOSS in GIS related or mapping problems. Obviously, the on-going awareness of FOSS tools in GIS community helps with further expansion of these tools to new applications and solving other problems. Wheeler (2012) states that "Many quantitative studies have shown that, in many cases, using FOSS programs is a reasonable or even superior approach compared to their proprietary competition.". Such instances may include the creation of spatial data from satellite imagery through desktop digitising methods but FOSS usually falls short when it comes to carrying out analysis. This is further emphasized by Kennedy (2011) who states that “with most proprietary products, FOSS may not provide a solution that will satisfy everyone’s requirements", therefore software evaluation is necessary. This is key for LCC as a clear evaluation has to be carried out and the software assessed in relation to the needs of the organisation and whether it can be helpful in the collection of property rates. According to Steiniger and Bocher (2008) the most common FOSS are shown in Table 2.

Table 2: Free or Open source GIS software

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Source: Steiniger and Bocher (2008)

2.2.2.2 Proprietary Desktop GIS Software

Proprietary software in GIS is software that can be obtained commercially and requires the periodic renewal of a user license. Proprietary GIS software is more user-friendly, and excels in the areas of spatial analysis, data manipulation, and data management. The table below shows some of the most common commercial GIS software and table show widely used spatial database management systems. Table 3 shows some common commercial GIS software and Table 4 the common commercial spatial database management systems.

Table 3: Common commercial GIS software

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Source: Formulated by Author (2017)

Table 4: Commercial spatial database management systems

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Source: Formulated by Author (2017)

Depending on the type of project being carried out by an organisation it is critical to properly analyse the various types of open source and commercial software that are available on the market. The expected outcomes or outputs also determine the kind of software that should be employed in the particular task being carried out. Aspects of model and costs, application support, skills availability, operational support options, performance requirements and risk analysis need to be taken in consideration by an organisation during the software selection process. The user experience and the comfort towards the particular software also plays a major factor as to whether the free or open source route should be taken or a proprietary software should be considered.

Table 5 highlights some of the advantages and disadvantages of using FOSS and commercial software.

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Table 5: Pros and cons of FOSS and Commercial software

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Source: Steiniger and Bocher (2008)

This research used the commercial software ArcGIS provided by the Environmental Science Research Institution (ESRI). The motivation behind the use of this platform was that it is scalable and provided better analysis capabilities as compared to the other proprietary and open source GIS platforms.

2.2.3 Data

Data is possibly the most consequential component of a GIS. A GIS will integrate spatial data with other data resources and can even use a database management system (used by most organisations to organise and maintain their data). According the National Land Service (NSL, 2007) the entire purpose of having a GIS is to store and analyse geographic data. The use of geographical data being key as it assists in better understanding the objects on the earth’s surface and how they relate to the problems faced.

According to Artz and Baumann (2009), the outcome is the emergence of the Geographic Approach—a new way of thinking and problem solving that integrates geographic information into how we understand and manage our planet.

This approach allows us to create geographic knowledge by measuring the earth, organizing this data, and analysing and modelling various processes and their relationships. The spatial approach also allows us to apply this knowledge to the way we design, plan, and change our world (Artz and Baumann, 2009).

2.2.3.1 Sources of Data

The sources of data can either be primary sources or secondary sources. In order to conduct a meaningful geographic analysis it is important to select data sources that are appropriate to the research question at hand. Similar in dealing with rates collection and the application of GIS, only with the correct data at hand can useful analysis be done and this in turn results into proper decision making as regards to rates.

2.2.3.1.1 Primary Data sources

This is first hand data obtained by carrying out actual field work. Primary data collection usually results in the creation of new data sets (NSL, 2007). The methods with which primary data is obtained include:

- Field surveys using handheld GPS equipment for a particular accuracy or alternatively differential GNSS equipment for more accurate data points.
- Data captured first hand using remote sensing techniques or aerial photogrammetry.

The advantage the primary data provides is that the collector of the data has complete control over the data sets. This ensures that the data are relevant and appropriate to the geographic questions being asked. The analyst can design and implement data collection procedures, can answer questions and address concerns as they arise, or alter the design at any point in the data collection process if necessary (NSL, 2007). The other advantage of using primary data is that the analyst has a deeper understanding and knowledge of the data collected. Every aspect of the data set is familiar and the data collector can recognize and identify any data anomalies immediately. They can answer every question about the data set or are able to ask the person who collected the data. This entails knowing the accuracy of the data, how current the data set is and it is easy to make accuracy assessments concerning measurements, and verify the accuracy of the attribute data.

Primary data comes with its own challenges. It requires careful thought and consideration when choosing a sample design. This requires the collector or analyst to have a basic understanding of statistical sampling techniques (NSL, 2007).The process of collecting the data can often be time consuming and costly. Depending on the complexity of the project, data collection may overshadow the actual GIS analysis. Primary data often require post-processing and error checking, which only adds additional time and cost to the project (NSL, 2007).

2.2.3.1.2 Secondary Data

Secondary data are a term used to describe data that have been collected by anyone other than the person doing the analysis. These datasets are collected from a variety of sources such as government agencies, private companies, or even files distributed freely over the internet. Secondary data sources include:

1. GIS Data - data that has been processed and already lives in a GIS database is considered secondary data. GIS data have a definite advantage for geographical problem solving since it is typically a simple matter to load existing data into a geographical database.
2. Tabular data - tabular data such as census data or property assessment data can be very useful in performing geographic analysis. There are large numbers of tabular data sets readily available for a variety of purposes. Tabular data are considered secondary data when another individual or another organization carries out the data collection. In the case of property tax and the use of GIS to maximize the collection of the same, tabular data will be data existing in the valuation roll.

The biggest advantage to using secondary data is time as datasets have already been collected and processed saving the researchers hours of time worrying over sampling design, and data collection. It is also much less expensive to acquire secondary data as compared to primary data.

Secondary data brings about its own challenges when carrying out research. The researcher is not familiar with the data set as he/she would have if they had been involved in the collection process. This means that the researcher must rely heavily on the accompanying documentation.

The researcher has no idea of the accuracy of the data unless it is explicitly defined. Acquiring data from organizations with a strong reputation for producing quality data will greatly improve the usefulness of the data.

There is no control over what attributes are collected or stored. Secondary data may also come in a variety of formats. In the best of cases secondary data will already come in a geodatabase or any one of the common file formats for storing geographical data. Data may also come in formats that will require some manipulation before they can be used. Some examples of these are JPEG files, TIFF files, PDF files, and Excel tables. These forms of secondary data require a great deal of processing before they can be used, but sometimes they may be the only source (NSL, 2007).

In essence when it comes to data Tomlinson (2013) states that “knowing what information products you want, you can identify the data required to make them and plan for the acquisition of the needed data. What can be obtained that already exists? What can be obtained that already exists? What can be created from existing sources? And What levels of accuracy and scale will be required?. In the use of GIS for rates collection, both primary and secondary data are key.

2.2.4 People

GIS users range from technical specialists who design and maintain the system to those who use it to help them perform their everyday work. In an organisation, such as a city council, a diverse spectrum of professionals can make use of the GIS technology. These range from IT specialists, City planners, and Engineers. GIS technology is of limited value without the people who manage the system and develop plans for applying it to real-world problems (ESRI, 2017). These people need to be well trained and knowledgeable and skilled in the use of GIS platforms and in carrying out spatial analysis. Tomlinson (2013) states that a well running GIS requires people with skills of geographic analysis, problem solving, technical capabilities and above all must be able to think.

2.2.5 Methods

Methods or procedures refers to the way people do their jobs and the changes they will have to make to do their jobs using a new GIS system (Tomlinson, 2013).

A successful GIS operates according to a well-designed plan and business rules, which are the models and operating practices unique to each organisation. For organisations such as City councils, this entails incorporating the technology with respect to the core operations of the Councils which all aligned to improving services and maximizing revenue collection.

2.3 Functions of GIS

General-purpose GIS software performs six major tasks such as input, manipulation, management, query, analysis, and Visualization.

2.3.1 Input

The important input data for any GIS is digitized maps, satellite imagery, spatial data and tabular data. The tabular data is generally typed on a computer using relational database management system software. Before geographic data can be used in a GIS it must be converted into a suitable digital format. The Database Management System (DBMS) can generate various objects such as index generation on data items, to speed up the information retrieval by a query (ESRI, 2013). The process of converting data from paper maps into computer files is called digitizing (ESRI, 2013). Modern GIS technology has the capability to automate this process fully for large projects; smaller jobs may require some manual digitizing using on-screen digitizing. The digitizing process is labour intensive and time-consuming, so it is better to use the data that already exist as long as the data formats, accuracy and collect methods of the data are properly known and understood.

Today, many types of spatial data already in existence in GIS-compatible formats. These data can be obtained from data suppliers and loaded directly into a GIS (ESRI, 2013).

2.3.2 Manipulation

GIS can store, maintain, distribute and update spatial data associated text data (ESRI, 2013). The spatial data must be referenced to a geographic coordinate systems. The tabular data associated with spatial data can be manipulated with help of data base management software. It is a common practice that the data types required for a particular GIS project will need to be transformed or manipulated in some way to make them compatible with the system. For example, geographic information is available at different scales (scale of 1:100,000; 1:10,000; and 1:50,000). Before these can be overlaid and integrated they must be transformed to the same scale. This could be a temporary transformation for display purposes or a permanent one required for analysis. And, there are many other types of data manipulation that are routinely performed in GIS. These include projection transformation, data aggregation, and generalization.

2.3.3 Management

Management of spatial data is a key component of GIS. For small GIS projects it may be sufficient to store geographic information as computer files in form of a shapefiles or even in a stable format in a geodatabase. However, when data volumes become large and the number of users of the data becomes more than a few, it is advised to use a DBMS to help store, organize, and manage data. A database management system (DBMS) is a collection of programs that enables users to create and maintain a database. The DBMS is a general-purpose software system that facilitates the processes of defining, constructing, manipulating, and sharing databases among various users and applications. Defining a database involves specifying the data types, structures, and constraints of the data to be stored in the database (Elmasri and Navathe, 2011).

DBMS come in various forms, but the relational model database management systems are the most useful when combined with a GIS. In the relational model, data are stored conceptually as a collection of tables and each table will have the data attributes related to a common entity. Common fields in different tables are used to link them together with relations. Because of their simple architecture, the relational DBMS software have been used so widely. Some DBMS examples include MySQL, PostgreSQL, Microsoft Access, SQL Server, FileMaker, Oracle, RDBMS, dBASE, Clipper, and FoxPro.

DBMS are flexible in nature and have been very wide deployed in applications both within and without GIS.

2.3.4 Query

The stored information either spatial data or associated tabular data can be retrieved with the help of Structured Query Language (SQL). Depending on the type of user interface, data can be queried using the SQL or a menu driven system can be used to retrieve map data. For example, you can begin to ask questions such as:

- How many properties have a particular land use?
- Where is the nearest payment point to a particular area?
- Where is the location of the area with the lowest payment statistics?

Both simple and sophisticated queries utilizing more than one data layer can provide timely information to officers, analysts to have overall knowledge about situation and can take a more informed decision.

2.3.5 Analysis

The power of GIS is fully appreciated when it dealing with the analysis of geographic data. The processes of geographic analysis often called spatial analysis or geo-processing uses the geographic properties of features to look for patterns and trends, and to undertake "what if" scenarios. Modern GIS have many powerful analytical tools to analyse the data such as over analysis, and proximity analysis.

2.3.6 Visualization

GIS can provide hardcopy maps, statistical summaries, modelling solutions and graphical display of maps for both spatial and tabular data. For many types of geographic operation the end result is best visualized as a map or graph. Maps are very efficient at storing and communicating geographic information. GIS provides new and exciting tools to extend the art of visualization of output information to the users.

The Figure 3 provides a summary of the key functions that are carried.

Abbildung in dieser Leseprobe nicht enthalten

Source: Malczewski, (1999 P.17)

2.4 ArcGIS

ArcGIS provides a scalable framework for implementing GIS for a single user or many users on desktops, on servers for use in the enterprise and across the Web, and in the field (ESRI, 2008). ArcGIS is an integrated family of GIS software products for building a complete GIS. ArcGIS offers a unique set of capabilities for applying location-based analytics to your business practices. Gain greater insights using contextual tools to visualize and analyse your data. Collaborate with others and share your insights via maps, apps, and reports.

It consists of four primary frameworks for deploying GIS as shown in Figure 4.

Abbildung in dieser Leseprobe nicht enthalten

Figure 4: ArcGIS Applications

Source: ESRI, 2008

2.4.1 ArcGIS Desktop

ArcGIS Desktop is a comprehensive set of professional GIS applications used to solve problems; to meet a mission; to increase efficiency; to make better decisions; and to communicate, visualize, and understand geographic information (ESRI, 2008). Simply it is a primary authoring tool for the ArcGIS platform, providing a primary platform for GIS professionals to manage their GIS workflows and projects and to build data, maps, models, and applications. Through these processes it becomes the starting point and the foundation to deploy GIS across organizations and onto the Web.

ArcGIS Desktop includes a suite of applications including ArcCatalog, ArcMap, ArcGlobe, ArcScene, ArcToolbox, and ModelBuilder (ESRI, 2008). According to ESRI (2013), with these capabilities and applications, three of the following things can be achieved:

1. Create, Edit, and Ensure the Quality of Geographic Data

Manipulation of data with a minimum number of clicks and automation of editing workflows with the powerful suite of editing tools provided by ArcGIS for Desktop. Advanced editing and coordinate geometry (COGO) tools can be used to simplify data design, input, and clean-up. Multiuser editing support makes it possible for several users to edit a geodatabase at the same time, facilitating data sharing between departments, organizations, and field staff.

2. Perform the Complete Cartographic Production Process within GIS

Using ArcGIS for desktop software one can easily create professional-quality, publication-ready maps by employing simple wizards, predefined map templates, an extensive suite of map elements, and advanced drawing and symbolization tools. The comprehensive set of cartographic tools in ArcGIS for Desktop automates many aspects of cartography, making map production a less time-consuming task. The completed maps can be saved, printed, exported, and embedded in other documents or applications.

3. Manage Data More Efficiently

ArcGIS for Desktop supports more than 70 data formats, so one can easily integrate all types of data for visualization and analysis. Creation of databases, definition of database schemes, and administering of the integrity of databases with an extensive set of geographic, tabular, and metadata management, creation, and organization tools. ArcGIS desktop also allows the user to disconnect from the database and edit in the field leading to the production of customized, accurate, publication-quality maps.

The other frameworks that form the parts of ArcGIS are described by ESRI (2008) in the following manner;

2.4.2 Server GIS

Server GIS provides the basis for building an integrated, multi-departmental system for collecting, organizing, analyzing, visualizing, managing, and disseminating geographic information. Server GIS solutions are intended to address both the collective and individual needs of an organization and to make geographic information and services available to both GIS and non-GIS professionals.

2.4.3 Developer GIS

Developer GIS provides customized and focused applications that allow many end users to leverage the full capabilities of GIS. It provides a comprehensive system for developing applications with GIS and enables a unified programming environment and tools that enable developers to:

- Embed GIS and mapping functionality in other applications.
- Build and deploy custom ArcGIS Desktop applications and extensions.
- Configure and customize ArcGIS products, such as ArcView, ArcEditor, and ArcInfo.
- Build Web services and server-based applications using ArcGIS Server and ArcIMS

2.4.4 Mobile GIS

Increasingly, GIS is moving from the office into the field by means of focused application solutions on mobile devices. Mobile GIS is the integration of GIS onto mobile devices which entails that GIS maps and applications can be carried into the field. This is revolutionary as data can be collected in the field and uploaded onto the main database. The mobile device can also be used to access enterprise information for a location thereby obtaining deeper knowledge and awareness. Mobile GIS also allows for tracking and coordination of team members in the field.

2.5 GIS in Developing Countries

Decision making at the national level in both developing and developed countries requires the integrated use of information from a multitude of sources. Developing countries have seen a growth in the interest and usage of GIS over the last two decades.

The shift from manual mapping methods to more advanced and computerized methods has been a welcome introduction. Both local and national governments in many developed countries have found GIS to be a critical tool in resource management, regional planning, and economic development (Mennecke & West, 2001).

According to Teefelen et al. (1992, p.11) the use of GIS facilitates easier, quicker solutions for technically complicated, time- absorbing geographical problems. Unfortunately, the practical use of GIS in many developing countries is hampered by the lack of accurate and detailed spatial and demographic data, political considerations, and management issues (Mennecke & West, 2001).

GIS is an appropriate administrative technology because, when utilized at both the national and local levels, its capabilities for managing attribute and spatial data can be used to better manage important national resources in the context of their location. In this way, GIS has great potential for use as a coordination tool that facilitates more efficient data collection, data management, and planning. While GIS cannot address the entire range of strategic decisions faced by the government of a developing country, they do provide capabilities that make them suitable for this use without precluding the use of more traditional strategic decision support systems (Brudney & Brown, 1992; Drummond, 1995; Grupe, 1992; Mennecke and West, 2001; Worrall, 1994). In fact, modern commercial GIS products can often be seamlessly integrated into existing information systems both at the client level as well as in organisation-wide systems (ESRI, 2017).

Despite a good number of research papers stating a strong case of the advantages of using GIS, governments of developing nations are still struggling in fully applying this technology in their day-to-day operations. Ottens (1992) observes that the application of technology to real world problems does not fundamentally differ between developed and developing countries. The challenges that developing nations face range from institutional barriers to technical constraints not leaving out limited human resource capabilities. Zeller (2002) categorised these challenges into Cost, Education, Infrastructure, and data constraints.

Despite the availability of open source GIS software, vendor-based platforms provide better capabilities for analysis and have a costly price-tag with an added cost of renewing the licenses. The appropriate technology should be low in capital costs and utilize as many local resources as possible and can be maintained without a high level of expertise (Burrough, 1992). The cost of training is also a major drawback- GIS specialists are not easily found and even those with basic knowledge on the use of the technology still struggle in the area of analysis. The way in which this problem is dealt with varies. In quite a large number of GIS projects local staff is selected and sent abroad to attend GIS courses.

In other projects the training is conducted “on the spot” (Teefelen et al, 1992). Although the number of computer training institutes is rapidly increasing, the number of people that have mastered computer skills is very low (Zeller, 2002). Data is a key component of GIS- in particular spatial data is costly and more complicated to collect as compared to attribute data. The nature of spatial data makes collection efforts difficult even in small, developed nations. As a consequence, there is currently only a limited amount of spatially referenced data available to support analysts and decisions makers working in developing countries (Mennecke & West, 2001). Gerland (1996) also adds that, “The problem remains that very little of this information is spatially referenced and organized, making it difficult or even impossible for analytical studies, monitoring, planning, and decision-support to take place”.

Even though collecting spatial data can be expensive there are strategies and techniques to reduce these costs and promote wider use of GIS (Mennecke & West, 2001). In particular, Global Navigation Satellite Systems (GNSS) and remotely sensed data offer the potential to provide vast quantities of useful data for developing countries at relatively low cost.

Modern GNSS technology allows users to utilize inexpensive devices to record their position. By equipping government or agency employees with GNSS devices the location of objects of interest can be recorded as a routine part of employees’ jobs. By investing in slightly more sophisticated, yet still affordable, hardware and software employees can link GNSS inputs directly to laptop computers that can record specific attribute data as well as a geographic location (Mennecke & West, 2001).

2.5.1 GIS in Zambia

The Survey Department of the Ministry of Lands, Natural resources and Environmental protection (ZSD) is the official mapping agency in Zambia. Over many years, the Department has traditionally maintained a national mapping series at 1: 50,000 and derivative scales in analogue format i.e. printed paper maps (Moyo & Bwalya, 2001). This has been key to the growth of GIS in Zambia as the maps produced under ZSD provide accurate base maps for GIS mapping.

This is important for the councils (who are agents of the Ministry) to have access to this data as it is critical in the day-to-day operations and the task of managing the cities, though the process of accessing this data is tedious and laborious.

Moyo and Bwalya also address another important aspect of a Spatial Database Infrastructure (SDI)- “It is evident that most countries globally have been developing or desire to develop national SDIs in response to the ever-increasing demand for geographic and land information at national level for social and economic growth - Zambia is no exception.

There are clear mandates supporting initiatives to rapidly develop national SDIs and to integrate these into regional and Global SDIs”. An SDI is currently running where data can be easily accessed by interested organisations and has provided a quicker means of obtaining up- to-date maps for Lusaka city council that are important in the collection of rates.

Other organisations in Zambia that actively use GIS in their operations include the Electoral commission of Zambia, Wildlife Conservation Society, Forestry Department , Central Statistical Office, Lusaka Water and Sewerage Company, Ministry of Health, and the University of Zambia.

The few GIS-based studies that have been carried out in Zambia have looked at issues concerning; investigating the wetland change on the section of the Kafue flats flood plain wetland area in Southern province, assessing the effectiveness of the historical Landsat Multi Spectral Scanner archive and remote sensing techniques to estimate savannah vegetation structure change in eastern Zambia. Other studies included the use of GIS and Remote sensing to map mine wastes in the Copperbelt province to determine environmental change over time, and the application of GIS to develop a method that combined a tree-based decision support approach with Multiple-Criteria Evaluation techniques to target areas for tsetse fly control (Munyati, 2000; Yang & Prince, 2000; Limpitlaw, 2003; Symeonakis et al., 2007).

These research papers have been beneficial and are a reflection of how GIS can be a valuable technology for the government at every level and the nation at large.

2.5.2 GIS for Local Authorities

Local authorities on a daily operational basis make decisions that are directly and indirectly related to objects on the earth’s surface or spatial in nature. The decisions largely involve spatial elements such as land parcels, billboards, buildings, and roads, storm drains, planning zones, rivers, parks and management of markets. Mitullah (2004) states that the management of these spatial objects can be facilitated with the use of GIS. GIS merely provides a tool for efficient tax collection through expedited billing and collection processes as well as a check on defaulting tax payers.

The enforcement of necessary measures in order to collect taxes through administrative or judicial processes still remains in the hands of the local government. Areas where rates collection administration and enforcement are not addressed face potential challenges.

GIS would enable management of Local Authorities to have access to numerous data and information from one computer location. Data sets that were physically scattered at various units and departments would be stored in one place and information that were formally difficult and time consuming to generate can now be obtained in a matter of seconds. This was noted in the case of local authorities in South Africa where Dhlamini (2011) state in recent years, the use of GIS in local government in South Africa has risen tremendously. Many GIS firms have developed innovative geographical information solutions that have great potential to provide decision-makers with the spatial information they need to address the myriad of problems affecting South Africa.

The uses for this technology provides a lot of potential to the managers at local government level to strategically plan for the collection of rates. GIS technology allows the user to confirm in real time the information provided by the taxpayer and adjust the amount of taxes and licenses, even if taxpayers did not declare the business activity or the real size of the property. In addition, it also brings benefits to citizen: taxpayers can know the state of their finances through the web and mobile applications, and process quicker transactions online such as exonerations, building permits, land use permits, property and license declaration, reports, inspections, etc. (Uraia, 2014).

2.6 Property Rates/Property Tax

Property tax is an annual tax imposed on real property usually by reference to an ad valorem tax base (i.e., the tax is calculated according to the value of the property) (FAO, 2002). This is inclusive of all tangible real estate property i.e. house, office building and the property he has been rented or leased out to others. Property tax has been in existence for at least three millennia. It is common throughout the world and has often been the subject of political debate. The strengths and weaknesses of this type of tax are well known and possibly more widely understood than any other tax (FAO, 2002).

The property tax is considered to be a good tax for local governments, mainly because of the connection between the types of services funded at the local level and the benefit to property values (Slack, 2011). The property tax remains as the most important revenue source for municipalities. It is the only tax that municipalities have the authority to collect.

2.6.1 Property Tax Mapping

An efficient real property tax administration depends on data that is accurate, timely and economical to maintain. Building and maintaining the property inventory and attribute database are the most labor-intensive and costly function of the property tax administration. Tax mapping is a core element of any integrated real property administration and taxation management system (Pareta, 2017). It is used to establish the link between the real properties (land and any property attached directly to it) in the field and the property assessment and tax records of the tax administration.

Tax mapping is a classical method of field operations for identifying real property units or “tax parcels” (Pareta, 2017). Tax parcel maps are fundamental to the appraisal (valuation) of real estate. They help to determine the location of property, indicate the size and shape of each parcel, determine actual land use and help to discover undeclared properties for taxation purposes. Although tax maps serve as a general reference to property locations, they are not a substitute for official cadastral survey documents and should not be used in legal land disputes. They allow the establishment of a real property record system that can be adapted to data computerization (Schall and Becker, 2009).

Digital tax parcel mapping is the process of converting the paper map based part of the “fiscal cadaster” (cadastral base maps, property identification maps) into a digital form and maintaining and management the tax maps of local government units with the help of a GIS (Pareta, 2017). This is normally carried out parallel to the introduction of a computerized real property taxation management system. This process can be time consuming costly and not easy to manage sustainable, if process flow and purpose of the system is not analyzed properly from the start.

However, if based on a prudent local needs and requirement analysis, the rewards in terms of better property management and higher real property assessment efficiency are numerous. Especially as a visual tool to produce dynamic maps in concert with other tax collection efficiency measures increases in locally collected taxes can be achieved. This can widen the local tax base considerably and hence will increase the financial options and the independence of local governments to invest in improved social service delivery and better physical infrastructure. Digital tax maps require the related database are regularly updated. The best way of ensuring this is to link the digital tax maps to the database used by an integrated tax administration system (Schall and Becker, 2009). This also holds true for the need to update maps when further spatial sub-divisions, re-plannings or consolidations are undertaken.

2.7 Empirical Research: Design and Application of Similar Studies

The empirical research focused on concisely describing studies that have been carried out and are similar to the research theme in order to fill the gap identified in the research problem and objectives. The most important conclusion to be drawn and lesson to be learnt from the case studies is how their methodologies, methods, tools, software, outcomes and interpretations of the results were selectively used to implement the GIS based rates collection system for a City Council.

2.7.1 Application of GIS in Improving Tax Revenue from the Informal Sector in Bayelsa State, Nigeria

Odubo Ebifuro, Eniye Mienye, and Tonye Vivian Odubo in the year 2016 proposed the use of GIS to improve tax revenue collection with the argument that a pre-requisite to an effective tax administration was knowing who the taxpayers are, where they are located and whether they are active or inactive. Knowledge on the spatial distribution of informal sector businesses and tax compliance status is integral in improving informal sector tax assessment and efficient collection of taxes. The aim of this paper was to explore the potential of Geographic Information System (GIS) for improving the revenue base of Bayelsa state through modern approach to the administration of informal sector taxation.

The methodology used in the research comprised of data collection, analysis of the collected data and database creation and results analysis. The primary data was collected through field work by use of GPS to acquire coordinate positions and also by questionnaire administration while the secondary data involved the use of enhanced satellite imagery of automated data (zone data) acquired from the database of the Bayelsa State Geographic Information System(BGIS), text books, journals, internet, conference and seminar proceedings. Global Positioning System (GPS) device was use to take coordinates of the various locations of the businesses in the study area. These Coordinates were later uploaded into Microsoft- Excel. A satellite image with a resolution of 0.5m dated 2013 obtained from GeoEye and used in representing the study area. A mosaic was created and assigned spatial reference in order to geo-rectify the imagery. The prepared excel format of coordinates was uploaded to ArcGIS 10 where the coordinates were converted to point features and displayed on the satellite image. Addition the attribute data obtained was manually entered into the attributes table in ArcGIS 10 containing fields such as Type of business, Business Activity, Address, Name, Sex, Marital Status, State of Origin, Home Town, and Date of Birth. In payment compliance of each business owner in the study area, the Microsoft Excel was used. Data was presented in tabular, graphical, and narrative forms. In analysing the data, descriptive statistical tools such as bar graphs and pie charts were used complemented with percentages. The results show that using a desktop GIS platform (ArcGIS 10) can be used to visualise the locations of potential revenue sources and also have the vital information of these owners attached to the spatial location. This enables greater decision making and planning as regards to revenue collection

2.7.2 Enhancing revenue collection through integrated GIS database system with property (land) Classification using an algorithm

Muswii (2015) proposed the development of an integrated GIS database system with property classification with the aim of examining how GIS technology can enhance revenue mobilization and describes comprehensively the functionalities of a GIS application prototype developed for internal revenue mobilization. It gives detailed information of the developed functionalities of the application and the dependencies on GIS for effective revenue collection and land management.

The research methodology employed to design the system included a combination of structured systems analysis and design and object oriented analysis and design methodologies. The system database was designed using MySQL database and PHP was used to design the user interfaces. Quantum GIS (QGIS) was used to create the sample spatial data and was loaded. Automated valuation methodology was adopted from the Moore’s 2005 Computer assisted mass appraisal methodology whereby algorithms were designed and implemented. In the analysis and design, the researcher introduced a new business process idea in tax collection and administration using the Computerised county Revenue Mobilisation System (CRMS) model proposed. This model was designed in a MYSQL database and the spatial data was converted using a special open source spatial software called quantum GIS (QGIS). The graphical user interfaces were designed using both java and PHP. The proposed system was intended to support a full range of business processes on revenue mobilization ranging from land parcel and revenue zones management, land rates calculation and collection of generated revenue, creation and administration of revenue zones, easier reporting and audit of land and other revenue collection points. A menu based graphical user interface (GUI) was developed to enable easy and friendly user-system interaction. This was expected to increase acceptability and utilization of the system among planners and decision makers and enhance the efficacy of revenue planning and budgeting.

The results show that deployment of this application would enhance the revenue generation capacity of the revenue collectors, pluck all leakages in the revenue collection and management process and put the country in a better position to meet her obligation to citizens hence contributing to the achievement of the vision 2030. Additionally, with this system, tracking of revenue using spatial data was realized and this contributed a lot in addition of value to spatial data which has basically not been widely operationalized.

2.7.3 Database Creation for Tenement Rate Collection: The Role of GIS.

Oluwadare and Oja (2014) carried out a research study on the role of GIS in tenement rate collection in Olorunda Local Government Area of Osun State in Nigeria.

The aim of the study was to explore the potentials of Geographic Information Systems (GIS) for improving the revenue base of Olorunda Local Government Authority through modern approach to the administration of tenement rates. The paper provided a means of acquiring property tax records and creating a database which can be managed for efficient, effective, and enhanced collection of property taxes in the developing world. Global Positioning System (GPS) was used to acquire the geographic positions of the properties. Attribute data of the properties were sourced partly from the Rating Authority and from property owners.

The research methodology employed a combination of primary and secondary data sources. The primary data was obtained through interview and the administration of structured questionnaires.

For officials of the taxing authorities, the data collected relates to their opinions on how the various property taxes are being assessed, levied and collected. Furthermore, data relating to how much revenue is being generated from tenement rating, problems encountered in the process and the level of effectiveness of the exercise and ways of improvement were collected. GPS was used to determine the locations of 85 parcels of land, containing 116 buildings, spatially distributed in 8 selected wards in the study area. Secondary data sources include the official publication of the Internal Revenue Department of the Council and topographical maps of the study area obtained from the Office of the Surveyor- General of Osun State. In addition, satellite imageries were downloaded using Google Earth. These data sets enabled the actual shape and size of the properties to be derived. Descriptive data, property characteristics, ownership records and valuation/rental information were obtained from the Local Government Office in Igbona. The software adopted in the creation of Database was ArcView GIS version 3.2a. This enables union of digital maps and attributes tabular data that facilitates display, query, summary and organization of data in a geographical context. The methodological approach adopted in the creation of the database is in the following systematic order:

a) Acquisition of spatial and non-spatial data using the appropriate data acquisition methods;
b) Satellite imagery of the study area was acquired in bits through Google Earth, stitched together using Corel Draw 11 and saved in raster format;
c) The image in raster format was exported to ILWIS 3.0 environment for geo-referencing and digitizing. Out of the three available digitizing methods, on-screen digitizing was employed;
d) After the digitizing of all the layers, the layers were checked for self-overlap and dead­ends. The layers created include the major roads, rateable properties, buildings, wards, among others; and
e) The edited layers were exported to ArcView 3.2a software environment; tables for the attribute data were prepared using Microsoft Access and finally linked with the graphics to form the expected database.

The results of the study showed that the system of rate administration in the study area needed overhauling as the manual systems in use were not effective in keeping property tax records. With the method of data acquisition employed in the research study, coupled with that of storage, analyses and presentation, GIS was seen to be amenable to meeting up with the challenges faced in the collection of tenement rate. The results also show that tenement rating could be a reliable and consistent means of revenue generation for the government if the right approach is adopted using the GIS technology. Germane to the findings of this study, every rate authority office should maintain a GIS database for effective collection of tenement rate.

2.8 Summary

A careful analysis of the nature of the problem statement in the research study suggests that GIS is capable of providing a database system that can be employed in rates collection and provide a tool that enhances decision making. This can be achieved by the visualisation aspect that is obtained using GIS and the variations in the manner in which the spatial data can be analyzed in relation to the attributes attached to those spatial components thereby allowing for projections of current and expected rates returns.

Table 6 below provides a summary of the various methodologies, methods and tools used based on the findings in the case studies. The methods, tools and the methodologies used in these separate studies were careful looked at in the development and implementation of a GIS based rates collection tool for the city council keeping in mind the research objectives and the gaps identified in the case studies looked at.

Table 6: Review of case studies

Abbildung in dieser Leseprobe nicht enthalten

Source: Formulated by Author (2017)

This chapter presented the review of literature on how GIS can be applied to rates collection through the capability of a GIS platform being able to link spatial and non-spatial data and carrying out analysis.

The results obtained from the analysis would further used in the planning for rates collection and in decision making. It looked at how GIS provides a visual aspect in the process of rates collection and how this allows revenue managers obtain a holistic view of how rates are being distributed and how the rate payment analytics are mapping out. The advantages that GIS provides were further highlighted by drawing a comparison with traditional manual tasks.

The chapter also looked at the components that are required in order to have a full-fledged and successful GIS system. It focused on how the key ingredients of data, software, hardware, people and procedures are brought together to work harmoniously in order for the benefits of GIS to be appreciated. Focus was also given to the various GIS platforms that are available on the market (both commercial and proprietary) and how they weigh-up against each other and some of their advantages and disadvantages. The commercial desktop GIS software known as ArcGIS provided by ESRI was explored with its various components and suits as it was the platform of choice used in the research.

The use of GIS in developing countries was also looked into and some of the challenges faced in implementing GIS systems both at central government and local government level. Issues of costs and capacity were looked at and how if these can be overcome, GIS technology can help change in some areas and improve already existing systems at every level of government. Property rates or property tax was defined and how the fact that it has a geographical aspect to it requires that some form of mapping be carried out when managing the property rates.

The chapter was concluded by looking at three case studies of a similar nature carried out in other countries, the types of GIS platforms and databases that were used, the research methodologies, methods and tools used to carry these researches and the results obtained. These were used to draw similarities, lessons and outcomes that were selectively used in the development of a GIS system for rates collection.

The next chapter examines the methodology that was used. The literature review was foundational in developing the framework for the design, data collection and analysis.

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 Research Design

This chapter examines the overall approach that was taken to carry out the research process. The methods were selected in such a way as to ensure the objectives of the research were achieved. It advances how the problem was investigated and describes the tools used to undertake the research study.

The research design provides the framework within which a particular research study will be conducted. It constitutes the outline for the collection, measurement and analysis of relevant evidence with minimal expenditure of effort, time and money hence it is critical to the success of the whole undertaking.

It provides the framework that specifies the type of information to be collected, its sources and collection procedure. Kinnear and Taylor (1996) define research design as the blueprint that is followed to complete the study whilst Churchill and Iacobucci (2005) define it as the process put it place to ensure that the study is relevant to the problem and will use economical procedure. While conducting the present study, care has been taken to incorporate these concepts in the research deign. The research design was not fixed but flexible and directed attention on the following:

a) Specifying the objectives with sufficient precision.
b) Selecting appropriate methods and tools by which data is to be collected.
c) Carrying out the design of the database system.
d) Data collection, processing and analysing.
e) Finally, reporting formats for the findings.

The methodology used for the research study was divided into the following segments:

- A general assessment of the business processes and procedures, existing data and the formats of the datasets, goals and objectives and proposed system use.
- Collection of data from primary and secondary sources. This includes shapefiles, existing hardcopy maps, satellite imagery, attribute data from reports and spreadsheets.
- Data development and conversion of both collected and existing data in GIS database. These tasks include database editing, scanning of hardcopy maps, geo-referencing and digitising from the satellite imagery.
- Application development of the GIS database for rates collection and MySQL database. Design of the geo-spatial database based on the general assessment and MySQL data models that support object relationships between the two databases. This includes analysis and report generation modules, integration of the GIS database with the MySQL database and visualisation of the data on a GIS graphical user interface (GUI) application. Figure 3 shows the complete methodology flow chart.

Abbildung in dieser Leseprobe nicht enthalten

Figure 5: Methodology flowchart

Source: Formulated by Author (2017)

3.2 Needs Assessment

The needs assessment focused on understanding and appreciating the business procedures and processes carried out at LCC. This looked into the types of datasets that are used and the formats they are in, the existing rates collection system and how all of these come together in order to achieve the set goals and targets attached to rates collection.

The purpose was to determine and address the gaps in the current methods to and to provide an improved system for better performance or to correct a deficiency.

3.3 Data Collection

The research involved the collection of both primary and secondary data that was required in the development of the GIS based rates collection tool or system. Five ground control points were obtained using the CHC X91 differential GNSS survey equipment from the study area. Other types of spatial data obtained included the area cadastre in form of shapefile was obtained, streets shapefile, satellite imagery, and the city topographic map. The non-spatial data obtained was the valuation roll data covering the area of study. A semi-structured interview was used to obtain information on how the process of rate collection takes place.

The rates unit uses two data types - spatial and non-spatial data in the rates implementation. The spatial datasets describe those objects that are linked to specific locations such as land parcels, buildings, and streets. The non-spatial data contains the attribute information that describes those spatial data such as the property address, property ownership, occupant, taxpayer, land use and building type. All these datasets were captured or collected as part of the database design. The capturing process involved mapping and collecting current and relevant property information on all properties within the study area for the purpose of developing an effective system.

3.4 Data development and conversion

The research data collected was then processed in order for it to useful in the databases developed. The control points obtained in a projection of UTM 27 on Arc 1950 were uploaded in the ArcMap (a suite application of ArcGIS) and used to georeference the satellite imagery of the study area. The satellite image was then used to digitise all the buildings and structures, property boundaries in the study area. The cadastre shapefiles were then overlaid on the digitised data for validation purposes. Further validation was carried out by overlaying the City topographic map to ensure that the data produced was consistent with the official mapped information from the city topographic map. The data obtained showing the streets in the study area was created using a different datum (Arc 1960); using the projection capabilities of ArcMap the projection was changed to Arc 1950 in order to have data in the same system.

The non-spatial data from the valuation roll was obtained in portable Document Format (pdf) which is not suitable for a database system. It was converted to spreadsheet format for further use in a database.

3.5 Database System development: Database design

The development of the database design involved two types of databases. A geodatabase housing the geographical data and a relational database that stored data tables and information from the valuation roll.

The database design phases were carried out in a systematic manner to ensure that the resulting database met user requirements, had efficient data structures and retrieval mechanisms.

The process for the relational database design for the property rates data was carried by clearly defining the column elements in the valuation roll, how these elements relate to each other, and designing an Entity Relationship diagrams (ERD) that showed the flow of the objects in the database using the appropriate relational database design software.

The Geodatabase developed had spatial data stored in layers of property boundaries, streets, buildings, and a satellite image. The property rate database stored information on property ownership, Land use, the rateable value of the property and the township or neighbourhood where the property is located.

3.5.1 Database System development: Desktop GIS and MySQL

The implementation of the system was carried out using both commercial and open source software. ArcGIS was used for the spatial database and MySQL to create the relation database. ArcGIS desktop suite was installed on a personal computer and used to create the geodatabase. The data in the geodatabase was stored in form of feature datasets and feature classes. A feature dataset is a collection of related feature classes that share a common coordinate system.

Feature datasets are used to spatially or thematically integrate related feature classes. Their primary purpose is for organizing related feature classes into a common dataset for building a topology, a network dataset, a terrain dataset, or a geometric network (ESRI, 2017).

Feature classes are homogeneous collections of common features, each having the same spatial representation, such as points, lines, or polygons, and a common set of attribute columns, for example, a line feature class for representing road centerlines. The four most commonly used feature classes in the geodatabase are points, lines, polygons, and annotation (the geodatabase name for map text) (ESRI, 2008).

The spatial features in the geodatabase had attributes attached to them such as property number, property size, property perimeter and the name of the streets. The user interface for the system is an easier to use ArcGIS desktop free platform extension or application known as ArcReader. Through this platform one can carry out a property search and any information attached to the property but data editing cannot be carried out on this platform.

The relational database used for the property rates information was developed using MySQL. The relationship tables in the database were all designed using MySQL workbench. Through this application data modelling, SQL development, and comprehensive administration tools for server configuration, user administration, and data backup can be carried out.

3.6 System Design and Implementation

The following sections describe the design of the GIS based rates collection tool in more detail and the process of implementation. Key to the design of the system, the sections describe the existing system of property rates collection at LCC, identify the needs for the proposed GIS based rates collection tool, the GIS hardware and software suitability/scalability, application development before the system is finally adopted and implemented.

3.7 Existing System

The Lusaka City Council has a mandate of collecting property rates (or property tax) from residents and in turn provide services such as well-maintained roads, street lighting, and waste management. Without constant and predictable revenue, LCC is unable to provide the services that are required in the community.

The city or council decides its budget, then subtracts revenues it expects to collect through other taxes, licenses, and fees. The remainder—the amount to be raised from the rates—is divided by the total rateable values for the city or district, giving the actual tax rate to be applied to the assessed value of each property (Strasma et al. 1995).

The Lusaka City Council’s jurisdiction for property rates collection covers the entire district of Lusaka. These properties only includes properties that have been offered by either the Lusaka city council or the office of the commissioner of lands under the Ministry of Lands, Natural Resources and Environmental Protection. Due to the mushrooming of unplanned settlements this “catchment area” is further reduced to properties that have been surveyed and are on title deed. LCC has divided the city boundaries into forty (40) property rates collection areas. The other areas in Lusaka city that are not part of the forty areas are part of the settlements that are unplanned therefore they do not contribute towards property taxes. These areas are charged ground rent which is based on the type of legal document that these areas obtain (which is known as an occupancy license based on the type and nature of the structure built and does not recognize any property boundaries). Table 7 shows the area demarcations that are used by LCC in the process of rates collection.

Table 7: Area codes for Rates collection

Abbildung in dieser Leseprobe nicht enthalten

Source: LCC (2017)

The current processes in rates collection at LCC are as follows:

- A valuation roll is prepared that shows the rateable values of properties that are eligible to contribute towards property taxes. The roll contains information on the property number, owner of the property, land use, and the description. The valuation roll is prepared based on the area code demarcations shown in table.
- Once the valuation roll is completed the rateable value is used to calculate the actual rate amount to be paid. This amount is derived based on the land use which can be either residential, commercial, Institutional or industrial.
- When the rates are determined the rate bills are prepared and given to the mail-runners who are tasked with the duty of distributing the bills. The mail-runners are divided based on the city area divisions.
- Before the bills are distributed, the mail-runners collect hard copy maps for the particular area they are targeting from the survey section of LCC in order to make copies of the areas where bills need to be distributed.
- The hardcopy maps contain information on property extents, property number and street name for townships.
- During the distribution of the bills the client is given a register to sign on receipt of the bill.

This process does not have any GIS involved in it making it difficult to plan for the distribution or even to account for how many bills have been distributed and no visualisation of the rates payment patterns of council’s clients. Another challenge with this process is that it solely depends on hardcopy maps that are not updated and are not even dynamic as those that can be created using a GIS platform such as ArcGIS. The hardcopy maps do not account for the changing land uses and even the any subdivisions, consolidations, re-plannings or plot creations that may have taken place in the city.

Figure 6 shows a copy of an actual bill that is given to clients by LCC. The bill slip shows the breakdown of the annual levies paid on the properties and the half year demand formula as follows:

Annual levy is: 0.1 Ngwee in Kwacha for residential properties.

Abbildung in dieser Leseprobe nicht enthalten

Figure 6: LCC Rate Bill

Source: LCC (2017

3.8 Image Data

Image data was key in the research as it formed an important aspect when creating new data (through digitising) and validating the data. Table 8 shows the image data that was available for the research study.

Table 8: Image Data

Abbildung in dieser Leseprobe nicht enthalten

Source: Author (2017)

Figure 7 shows a satellite image of the study area with the specific target area enclosed in a red boundary.

Editorial note: Figute 7 was removed due to copyright issues.

Figure 7: Study area boundary overlaid on a satellite image

The use of these data formats as base maps is a key component of working with GIS and in the development of a GIS based rates collection tool. The area of study was divided between areas 20 and 14. The selection of the study area was because this area encompasses both residential, commercial and institutional land uses. This provided a sample size that included the various types of property types that are rated by LCC.

According to Ayeni and Adewale (2006), the quality of the results produced from GIS analyses and applications ultimately resides in the quality of the data used.

The study area was also overlaid on a topographic map sheet of the study area in order to relate it to the initial city plan and mapped areas. This was a key in validating the collected data as the topographic maps provide a benchmark or point of reference.

Figure 8 shows the location of the study area on sheet number 1528 A4 of the topographic maps of Zambia which covers Lusaka district. The blue demarcation line shows the area of study (area 20)

Abbildung in dieser Leseprobe nicht enthalten

Figure 8: Study area boundary overlaid on the City topographic map

Source: Zambia Survey department (ZSD, 1986)

3.9 Vector Data

The availability of accurate vector data is very important in the use of GIS for rates collection. The vector data formats mainly used in property rates are mainly polygon features to represent property boundaries and line features representing streets. Issues of the accuracy of the vector data are very important hence consistency was cardinal when storing the data. All the data was collected or projected to the datum Arc 1950. These were stored as feature datasets and feature classes. Table 9 shows the vector datasets used in the research and the associate attribute table data. These datasets were opened together in ArcGIS as shown in Figure 9.

Table 9: Feature dataset and feature classes of the study area

Abbildung in dieser Leseprobe nicht enthalten

Source: Author (2017)

Abbildung in dieser Leseprobe nicht enthalten

Figure 9: Map showing properties and streets Source: LCC (2017)

3.10 Non-Spatial data

The non-spatial data that was cardinal in this research study was the use of the LCC valuation roll. This contained columns showing the Stand number, Street, Use code, Lease holder, Valuation date, Land value, additional/ Improvement value, and the rateable value. It also shows the area code that is specific to a particular township(s). Figure 10 shows an extract of the valuation roll for the year 2013 and consolidated in the year 2015. The valuation information obtained from LCC only covered the area of study for the research.

Abbildung in dieser Leseprobe nicht enthalten

Figure 10: Extract of LCC valuation roll

Source: LCC (2017)

3.11 GIS System Design

The design of a GIS based rates collection system was mainly around how the system is able to carry out functions. Functional requirements referred to what the system was supposed to do in terms of functionality and features. The system design entails linking the GIS to a relational database in order to link the data in the valuation roll to the spatial data in the geodatabase. The design functions needed to seamlessly incorporate into the council’s procedures without changing the daily business processes.

The database design is a crucial component of the system design and forms the foundation of all activities such as data entry, retrieval, editing, and visualisation of properties that are rateable. The design involved three-steps- The design of the database using MySQL, the geodatabase using ArcMap and the process of linking the two databases. The third and final step involved displaying the data on a graphical user interface application. The following sections below give details of the three steps in the database design process. The design takes into consideration the existing data, needs assessment and data models so that the resulting database meets the user requirements.

3.12 Geodatabase design

The spatial database used in the research was stored in a geodatabase created using ESRI’s ArcCatalogue of the ArcGIS suite. The software provides two options to either create a personal geodatabase or a file geodatabase. According to Childs (2009) whether one is working with large or small datasets, file geodatabases optimized for use in ArcGIS are ideal for storing and managing geospatial data. Whether one is working on a single-user project or a project involving a small group with one or several editors a file geodatabase offers structure, performance and data management advantages that are not provided by personal geodatabases and shapefiles. The structural strength of a file geodatabase entails improved versatility and usability, optimized performance and few size limitations. The performance advantage makes possible easier data migration, improved editing models and storage of rasters in the geodatabase. The data management capabilities of the file geodatabase entails customizable storage configuration, updates to spatial indexes, and the use of data compression (meaning there is no need to uncompress the data as ArcGIS and ArcReader can read it directly).

3.13 MySQL Database

MySQL is an open-source relational database management system (MySQL, 2017). In order to create relationship tables for the database MySQL workbench was used. MySQL Workbench provides data modelling, SQL development, and comprehensive administration tools for server configuration, user administration, backup, and much more. MySQL Workbench simplifies database design and maintenance, automates time-consuming and error-prone tasks, and improves communication among data base administrators (DBA) and developer teams. It enables data architects to visualize requirements, communicate with stakeholders, and resolve design issues before a major investment of time and resources is made.

It enables model-driven database design, which is the most efficient methodology for creating valid and well-performing databases, while providing the flexibility to respond to evolving business requirements.

Model and Schema Validation utilities enforce best practice standards for data modelling, also enforce MySQL-specific physical design standards so no mistakes are made when building new ER diagrams or generating physical MySQL databases. All these capabilities culminate into a database that is relatively free, easily available and allows multiple users in a single database. This can be helpful and cheaper for LCC instead of storing the data in excel that has numerous restrictions as compared to MySQL. Figure 11 shows the user interface of MySQL workbench.

Abbildung in dieser Leseprobe nicht enthalten

Figure 11: MySQL workbench interface

Source (Author, 2017)

The database created required to be secured using a unique user name and password. This is shown in the next figure 12 as these had to be entered before the database could be accessed.

Editorial note: Figure 12 was removed due to editorial reasons.

Figure 12: User name and password access into the MySQL database

Source (Author, 2017)

MySQL workbench was used to create a new model in order to create the relational database. Figure showing the database created (circled in red) with the default database name as mydb and the options to add tables below.

Abbildung in dieser Leseprobe nicht enthalten

Figure 13: Database Creation in MySQL

Source: Author (2017)

The next step that followed was the creation of tables. Most database design guidelines encourage the organisation of a database into multiple tables—each focused on a specific topic—instead of one large table containing all the necessary fields. Having multiple tables prevents duplicating information in the database because you store the information only once in one table. When you need information that is not available in the current table, you can link the two tables together.

For example, you might obtain data from other departments in your organization, purchase commercially available data, or download data from the Internet. If this information is stored in a table, such as a dBASE, INFO, or geodatabase table, you can associate it with your geographic features and display the data on your map. The planned database included five (5) tables namely: PropertyPrimary, OwnwershipLanduse, Valuation, tblHistory, and UseTable.

3.13.1 PropertyPrimary Table

This was the first table that was created. The objects in the table included the Stand Number (StandNo), the name of the street (StreetName), the area of the land parcel (AreaofLand), the identity of the record (RecordID), and the user table (UserTable). Figure 14 shows the creation of this table. The name of the object created was placed under column name (circled red in fig). Another important aspect was the defining of the datatype for the table objects for instance in Figure 14 the datatype selected for the Stand Number was variable character (VARCHAR) as stand numbered in Lusaka city are a combination of letters and numbers (for example LUS/3500 or F/284a/180). The Stand Number was also the unique identifier in this table or the primary key. Choosing a primary key is an important step in good database design. A primary key is a table column that serves a special purpose of ensuring row-level accessibility.

Abbildung in dieser Leseprobe nicht enthalten

Source: Formulated by Author (2017)

3.13.2 Ownership Land Use

The second table created contained ownership land use. The objects of the table included the Stand Number, the description of the property or the extent of development, the use code which can either be residential, commercial or institutional, the lease holder, and the ownership history. Figure shows the Ownership Land Use table that was created.

Abbildung in dieser Leseprobe nicht enthalten

Figure 15: Ownership Land use database table

Source: Formulated by Author (2017)

3.13.3 Valuation

The third table in the database created contained valuation information. This included the Stand number, the date of the valuation, the value of the land, the value addition, the rateable value of the property and the valuation ID.

Abbildung in dieser Leseprobe nicht enthalten

Figure 16: Valuation database table

Source: Formulated by Author (2017)

3.13.4 Tbl History

The fourth table created was the history table that contained objects that would have been helpful in creating an audit trail. The objects in this table were table information, field information, old and new value information, and the user identification as shown in figure 17.

Abbildung in dieser Leseprobe nicht enthalten

Figure 17: History database table

Source: Formulated by Author (2017)

3.13.5 User Table

The fifth and final table contained user information. The objects of this table included the user identification, the user password, the access level, email recovery and the record identification. This is shown in Figure 18. The field containing the user ID, user password and the access level had the Not Null (NN) marked. This table provides the key requirements of accessing the database and the users are restricted to those that are entrusted with the powers to be able to update the existing valuation roll or to add any information that is required through editing.

Abbildung in dieser Leseprobe nicht enthalten

Figure 18: User table database table

Source: Author (2017)

An Entity relationship diagram (ERD) was then generated in order to depict the relationships between the tables in the database that was created. In other an ERD provides the conceptual and representational model of data used to represent the entity framework infrastructure. An entity relationship diagram (ERD) shows the relationships of entity sets stored in a database. An entity in this context is a component of data. This conceptual design is independent of implementation the user can implement database in any DBMS using ERD data model. Figure 19 shows the ERD.

Abbildung in dieser Leseprobe nicht enthalten

Figure 19: Entity Relationship Diagram

Source: Author (2017)

In order to have an easy to use interface, Microsoft access was used to create an interface and to also connect to the database tables created in MySQL. An Open Database Connectivity (ODBC) was used to link the two. This is a standard application programming interface (API) for accessing database management systems (DBMS). Figures 20 shows the process of connecting the MySQL tables to Microsoft access where Microsoft access was activated and in the user interface the external data option was selected with the ODBC database as one of the available options.

Editorial note: figure 20 was removed due to editorial reasons.

Figure 20: ODBC database connection

Source: Formulated by Author (2017)

After selecting the type of database connection required two options were provided i.e. to import the source data into the new table in the current database or to link to the data source by creating a linked table. The second option was selected as shown in Figure 21.

Editorial Note: Figure 21 was emoved due to editorial reasons.

Figure 21: Linkage to created tables

Source: Formulated by Author (2017)

A user interface developed was developed that allowed access to the data in the relational database. This interface provides the tool to be used when adding new information to the valuation roll or if there is need to update or edit any existing information.

This interface formed part of the relational database that was connected to the geodatabase. This connection was carried out ArcCatalogue (which forms a part of the ArcGIS suite). This procedure of connecting the database tables is shown in Figure 22.

Abbildung in dieser Leseprobe nicht enthalten

Figure 22: Addition of database connection

Source: Formulated by Author (2017)

After the completion of the connections, the tables formed could be viewed as separate database tables in ArcMap as shown in Figure 23.

Abbildung in dieser Leseprobe nicht enthalten

Figure 23: Database tables in ArcGIS

Source: Formulated by Author (2017)

In order to validate and communicate the capabilities of GIS effectively, the two database attributes were linked together using a common element in both databases which was the plot number. Table 10 shows a model of a join that takes place in ArcMap.

Table 10: Link between geodatabase and relational database

Abbildung in dieser Leseprobe nicht enthalten

Source: Formulated by Author (2017)

ArcMap provides two methods to associate data stored in tables with geographic features: joins and relates. When you join two tables, you append the attributes from one onto the other based on a field common to both. Relating tables defines a relationship between two tables—also based on a common field—but doesn't append the attributes of one to the other; instead, you can access the related data when necessary. For this research the join feature was used as the attribute data needed to be attached. This is shown in Figure 24.

Abbildung in dieser Leseprobe nicht enthalten

Figure 24: Joining of database objects in ArcMap

Source: Formulated by Author (2017)

3.14 Graphical User Interface

The final interface for the user was a free ArcGIS application known as ArcReader version 2.1. ArcReader is a free, easy-to-use desktop mapping application that allows the user to view, explore, and print maps and globes (ESRI, 2017). ArcReader maps are stored as Published Map Files (PMF) by the ArcGIS publisher extension to ArcGIS for Desktop. PMF files preserve a live connection to the data, so the user’s data view is always dynamic (ESRI, 2017).

ArcReader significantly increases access to maps throughout all parts of an organisation, introducing a new way for individuals and enterprises to share information that was once limited to those with advanced GIS software on their computers.

According To ESRI (2017), ArcReader provides the following capabilities; Zooming in/out, panning, going to previous/next extent, viewing spatial bookmarks, viewing and printing previously authored maps layouts, searching with the Find tool, identifying features, Using the hyperlink tool, and measuring features. The user interface is shown in Figure 25.

Editorial note: Figure 25 was removed due to editorial reasons.

Figure 25: ArcReader 2.1 user interface

Source: Formulated by Author (2017)

3.15 DesktopGIS

This section provides details on the specific implementation of the GIS based rates collection system. Figure 26 below shows the specific implementation of the DesktopGIS rates system and the link between the two databases and the GUI. These stages indicate the connection of the two databases by the use of a common primary key and creation of themed maps, the housing of the data in a central server and finally the end user accessing this data on an application that is easy to use but unable to carry out any editing of the data and maps.

The geodatabase was designed in ArcGIS and the relational database that stores that valuation information are connected also using ArcGIS to provide data to the ArcReader user through a common server in order to properly manage the process of collecting rates. The GUI or ArcReader application provides the last access point for the data and the dynamic maps produced using ArcGIS are viewed through this same interface by the rates managers. This interface was not customized as ArcReader provides all the required tools that can aid effective rates collection.

Abbildung in dieser Leseprobe nicht enthalten

Figure 26: Link from databases to the GUI

Source: Formulated by Author (2017)

3.16 Limitations

The research had limitations that needed to be taken into consideration. These considerations are considered below:

- The design of the GIS based rates collection system was limited to the Lusaka City Council and the particular area that was used as the study area. This entails that it was unique to the business processes and procedures at LCC and unique to the setup of the study area.
- The study area only focused on one area covering 939 properties and the revenue type focused on was ground rates. Other area demarcations and sources of revenue could not be explored due to constraint of time for the research. The data collected also shows a reflection of the scenario when at the time the data was collection. Subdivisions, consolidations, and re-plannings that were implemented beyond the data collection period were not accounted for.
- Finally, the research study was limited to using ArcGIS for desktop and associated functionalities to deal with the research problem. No customized functions were used and only the basic capabilities so the platform were uses as opposed to creating fully- fledged spatial decision support system or purchasing an off the shelf package given the constraints on funding and time.

3.17 Summary

This chapter presented the methodology that was used in order to effectively carry out the research and address the research problem, the aims and the objectives. The cardinal issue in the methodology was the framework that describes how the methodology was carried out. This was carried out by assessing the needs of LCC, followed by the collection of data, then the data development and data conversion, development of the database system and finally application of the DesktopGIS.

The chapter further described the system design and implementation of the GIS based rates collection system. It broke down the current methods that are carried out in the process of rates collection and the need for dynamic maps in the process. This information was obtained through semi-structured interviews and institutional reports.

The system design involved the development of a geodatabase using ArcGIS to store the spatial data, followed by the relational database created using MySQL with a Microsoft Access interface for easier data management. The two databases were linked together using a common (primary) key and finally displayed using ESRI’s ArcReader application.

The final aspect of the chapter was the presentation of the research limitations and chief amongst them was the argument that the research was limited to LCC and the business procedures carried out at the organisation. Another important limitation to note was that the research did not cover the entire city limits or rateable areas but only covered a selected study area.

CHAPTER FOUR: RESULTS AND DISCUSSION

4.1 GIS Based Rates Collection System

This chapter presents the research findings from the design of the GIS based system and implementation. It also looks at how GIS can be used to create dynamic maps and presentation of the system features, and the evaluation and testing of the system. The chapter then concludes by discussing all the outcomes from the research study.

The aim of the research study was to develop a GIS property rates collection system for Lusaka City Council. This was intended to address fallibilities in the current methods of rates collection and tools being used. This approach was in line with modern concepts and approaches as learned from the empirical research which focused on concisely describing similar studies.

From the findings, LCC does not apply any GIS tools in the process of rates collection. The entire process has no graphical or map visualisation and totally depends on the use of hard copy maps. The hardcopy maps are outdated and the do not represent the actual scenario as it is on the ground. Apart from the outdated information on the maps used also have portions missing through wear and tear or the maps have gone missing or misplaced. This leads to a failure to locate properties during the process of bill distribution and also to note which properties have undergone any change through subdivision or consolidation. This greatly affects the amount of rates collected by the city council.

Based on these findings GIS becomes the link in the process of distribution of bills and also providing visualisation of the activities in the study area in terms of Land use, value and rateable value of properties and the payment information. The Table 11 shows the results from the digitising carried out in the study area with respect to the data collected about the study area.

Table 11: Digitisation feature results

Abbildung in dieser Leseprobe nicht enthalten

Source: Formulated by Author (2017)

Apart from digitising the plot boundaries using the satellite imagery, the buildings in each plot were digitised. A total of 2790 buildings were digitised. This means that the ratio of the number of digitised plots to the number of digitised buildings in the plots was 1: 3 (meaning there is an average of 3 buildings per plot in the study area).

From the finding it was observed that the GIS system with a desktop ArcGIS platform could be used to provide statistics on various aspects of property information in the study area and at the same time provide a visual aspect. Statistics on the number of properties that are used for either commercial, residential, or institutional properties were obtained as part of the results. Apart from land uses statistics were obtained also on the values of properties, rateable values and the distribution of rates in terms of amounts. Figure 27 below shows an SQL search carried out in ArcMap. This search was specific for commercial properties only and the results not only provide the figure but the actual plots are highlighted on the map.

Abbildung in dieser Leseprobe nicht enthalten

Figure 27: SQL search for commercial properties and visualisation

Source: Formulated by Author (2017)

The same procedure was carried out for properties that are used for residential and Institutional purposed and the results are shown in Table 12.

Table 12: Table showing the land uses in Study area using ArcGIS

Abbildung in dieser Leseprobe nicht enthalten

Source: Formulated by Author (2017)

The statistics from the valuation roll were also obtained to show the variations in the land uses and this is shown in Figure 28.

Abbildung in dieser Leseprobe nicht enthalten

Figure 28: Number of properties vs Use code chart based on the valuation information from study area.

Source: Formulated by Author (2017)

The information on the land uses is key in deriving the amount of rates to be paid on a particular property.

The combined information in ArcMap was further broke down in terms of the total amounts of rates to be paid by properties with particular land uses. Figure 29 shows the analysis carried out in ArcMap.

Abbildung in dieser Leseprobe nicht enthalten

Figure 29: Statistical analysis for commercial properties in study area

Source: Formulated by Author (2017)

From the figures obtained on the chart developed a total amount of K1, 127,097.67 is expected per year from commercial properties in the study area. This was also graphically displayed by the commercial plots being highlighted as shown in the background of figure 30 above.

The same procedure was carried in order to obtain the expected amounts in a year for residential properties and Institutional properties. The amounts obtained were K1, 247,757.535 and K27, 828.6 respectively. These statistical findings and summaries for residential and institutional properties are shown in Figures 30 and 31.

Abbildung in dieser Leseprobe nicht enthalten

Figure 30: Statistical distribution of rates in study area for residential properties

Source: Formulated by Author (2017)

Abbildung in dieser Leseprobe nicht enthalten

Figure 31: Statistical distribution of rates in study area for institutional properties

Source: Formulated by Author (2017)

ArcGIS was also used to create graphs that depicted the ranges of the highest and lowest amounts of rates to be paid in the study area and the number of properties that fall within that particular category.

From the finding the results show that 413 properties out of 1017 fall with the range of K0-K1, 534.667. This is shown in Figure 32 with the highest statistical bar representing the 413 properties also highlighted in the map information in the background.

Abbildung in dieser Leseprobe nicht enthalten

Figure 32: Number of properties with the lowest rate charges per year

Source: Formulated by Author (2017)

The same procedure was carried out to obtain the number of properties that fall within the highest rate amounts in the study area. The results show the only one property falls within the range K44, 503.333-K46, 000.00. This is shown in Figure 33.

Abbildung in dieser Leseprobe nicht enthalten

Figure 33: Number of properties with the highest rate charges per year

Source: Formulated by Author (2017)

4.2 Colour-Coded Map

From the data collected ArcGIS was used to develop colour-coded maps based on Land use, the value of rates to be paid by particular properties, the areas showing the distribution of rates and the value of properties in the study area. These maps area also known as choropleth or thematic.

4.2.1 Land use analysis map

Using ArcMap, a colour coded map based on land use was produced. The colours were with respect to the type of land use. Blue was representative of commercial use, red was for institutional purposes and yellow for residential purposes. Figure 34 shows the land use map produced.

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Figure 34: Land use colour-coded map for the study area.

Source: Formulated by Author (2017)

4.2.2 Rateable Value analysis map

The second resultant map produced was based on the varying rateable values. Having property rateable values in the attributes allowed for the creation of a map showing colour graduation based on rateable values. The higher the rateable value would be depicted by a darker shade of gold whilst the lighter shade would depict properties of lower rateable values. This is shown in Figure 35.

Abbildung in dieser Leseprobe nicht enthalten

Figure 35: Colour-code map based on rateable values of parcels

Source: Formulated by Author (2017)

4.2.3 Status analysis map

Another important resultant map that was created showed the status of the area covered making collection of rates effective and having a visual aspect of the areas that have been covered. The green colour represents areas where bills have been distributed, the yellow properties cover properties where bill distribution is on-going and the red area symbolizes an area where bill distribution has not commenced. This is shown in Figure 36.

Abbildung in dieser Leseprobe nicht enthalten

Figure 36: Colour-code map based on the status of bill distribution

Source: Formulated by Author (2017)

4.3 ArcReader Graphical User Interface

The visual perspective given in GIS provides an added advantage in the management of rates. Apart from the visual aspect, the linking of the map information to the valuation roll data entailed the information in the attribute table now included more data pertaining to a particular property. This is illustrated in figure 40 were the selected property highlighted property on the map now has more data attached to its row in the attributes table. This data includes the land use, value of land, the improvement value, and the rateable value. This is shown in Figure 37.

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Figure 37: The identify tool (circled red) used to view all attributes attached to a particular property.

Source: Formulated by Author (2017)

The other important aspects of the plot such as the area and the perimeter could also be accessed through the interface from the attributes. The dimensions could also be measured from a property of interest using the measurement tool.

All the maps produced were then saved in publisher map files (PMF) in order to have them on readable when using the ArcReader application. Saving the maps in this format was achieved by using the ArcGIS publisher extension. ArcGIS Publisher includes access to the ArcReader developer component for embedding ArcReader or creating custom applications for viewing Published Map Files. ArcReader can only read data through a PMF. The PMF supports access to all standard vector and raster formats supported by ArcGIS but it does not include the data (ESRI, 2017). Rather, the PMF references the data whether it is on a local drive, a shared network drive, or an Internet service. The PMF preserves a live connection to the data, so the user's data view is dynamic (ESRI, 2017).

4.4 Discussion of Results

From the findings it was established that manual methods are mainly employed at LCC in the process of rates collection. The Hard copy maps have not been updated in a long time and are not a true reflection of the number of properties that are existing on the ground. These maps do not provide any dynamic visualisation that is available through the use of GIS generated mapping systems. These findings were summarized as the gap that needed to be tackled in order to successfully resolve the challenges currently being experienced in managing rates at LCC. The findings above looked at the shortcomings in the current methods, techniques and tools being used to manage the process of rates collection.

The findings also show the visual prowess of using a GIS platform as a satellite image can be overlaid in order to view the amount of development and carry out digitising of plot boundaries and actual buildings within the plot. This aided in developing a ratio of the number of buildings per plot thereby giving estimations and projections of the amounts of rates that can be expected in the study area.

The findings also revealed the gaps in the data on the number of plots in the study area. Comparison of the data collected in the valuation roll, the cadastre index and that collected through the study show that 59 plots were not accounted for by the valuation roll in the study area and 109 by the cadastre index. This is maybe alluded to the challenge in the flow of information on planning activities such as subdivisions or consolidations that are not accounted for in the valuation roll as soon as they are implemented. This in effect affects the total amount of rates that can be collected in the study area.

The use of ArcGIS database allows for a connection to be made with the MySQL relational database containing the valuation roll information thereby connecting the attributes and information. The major challenge in linking the data was some plots that were mismatching due to the different format in which the primary key (which was the plot number) was recorded in either the geodatabase or the relational database. For instance is a number was recorded as in one database as F378a/A/294 and in another as F378A/294/A but depicting the same property therefore the joining in ArcMap could not be carried out.

For the properties that matched perfectly it meant that the statistics on the specific land uses in the study area could be obtained and at the same time viewed on the map. This is key in the process as it allows for informed decision making on the end of the rates managers on the various uses on the properties and how they are distributed within the study area. Majority of the land use in the study (69.1%) is recorded for residential purposes, 30.1% is for commercial purposes and only 0.8% of the land use is for institutional purposes in the study area. This information can also assist in tracking who is complying with the specific use designated for the property and thereby paying the correct land rates.

The statistics in the finding also show that the highest amounts in summation are obtained from the properties used for residential properties followed by commercial and institutional properties respectively. This was because the residential properties were the highest in number therefore more rates were expected as compared to properties being used for commercial and institutional purposes. The difference in the rates expected between the residential and commercial properties was K120, 659.865.The difference was not significant despite that big difference in the number of properties. This can be attributed to the reason that commercial properties are expected to pay higher amounts as compared to residential properties.

The ArcGIS platform was employed to develop colour coded maps with the aim of visualizing trends and investigating area patterns during presentations or ad hoc analyses. The trends looked at included the land use, rateable values and bill distribution area. These trends are based on the row of information attached to each property. This was to counter the current scenario where personnel directly involved in rates rely on reams of printed material, hand- drawn maps and their own local knowledge and know-how to consider alternatives and make choices.

The colour-coded land use map developed shows the trends created using four distinct colours to show the variations in the land use of properties in the study area. The purple colour was of unique significance as it shows the plots that were not joined when linking the two databases due to the different formats in the plot numbers. A total of 110 properties could not be linked in the database. This is an important aspect as consistency needs to be maintained in the manner in which the plots are numbered in the databases.

The rateable value colour coded map shows the variations in the rateable values based on colour graduation system. The darker the shade of gold entails the higher the rateable value of the properties with the lighter shades depicting a reduction in the rateable values. This directly translates into the actually rates to be paid on the properties. The properties that do not have any colour graduations represent the 110 properties that could not be linked when joining the two databases.

ArcGIS was also used to produce a bill distribution status map that displayed difference colours based on whether bills have been distributed in a particular area. This is an important aspect as this visualisation helps the rates managers account for which areas have received bills and to strategically plan for the distribution. These colour variations can also be used to show who has paid and who is owing, which can aid LCC either penalize those defaulting or even plan for sensitization programs in areas where there is apathy towards the payments of rates.

ArcReader was the application used in the final interface for displaying and carrying out a search on any property. Whilst the standard graphical user interface for the ArcReader software provided for customization, this process was not carried out because the default interface was sufficient for the tasks. This backend entry application (ArcGIS) to the spatial data however, required specific GIS knowledge and skills. ArcReader application provided a user-friendly interface that had the required basic tools and capabilities such as view maps, print maps, zoom in/out, pan, go to full extent, go to previous/next extent, identify (multilayer), Switch between data view and layout view, View spatial bookmarks, Find, View/Print pre authored map layouts, Open/Close published map files, Show recently opened maps, Measure, Hyperlink, Magnifier window.

On the computer screen map users can scan a GIS map in any direction, zoom in or out, and change the nature of the information contained in the map. They can choose whether to see the roads, how many roads to see, and how roads should be depicted (ESRI, 2007). The added advantage is that the application can be freely downloaded from the ESRI site thereby reducing the cost of implementation.

With the use of a GIS platform, LCC is better placed in maximizing the process of rates collection and properly plan for it with both spatial and non-spatial data connected through a single platform.

CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

This chapter aims at drawing a conclusion to this study and provide recommendations on what can be carried out to further improve on this research paper. The conclusions are drawn from the findings, analysis and presentations of the research study. The recommendations arise from the findings and brings forward opportunities for further research that will improve the GIS based tool for rates collection.

5.2 Conclusion

When dealing with spatial data, GIS provides the most viable technology. In the case of LCC, this technology was successfully applied to the study area in order to carry out various analysis and produce maps that can assist in creating an effective means of rates collection. The GIS data available at LCC was linked to the data in the valuation roll in order to show that a connection between these two data sets via databases provides an advanced and sustainable means for the city council to manage the rates. The conclusions from this study are;

- The manual methods currently used for rates management and planning at LCC are not sustainable and do not ensure maximum collection of rates.
- Creation of a city geodatabase and linking this to valuation information allows for analysis to be carried out and based on this analysis decisions can be made concerning rates. This is confirmed by the capability of the GIS in performing queries carried out in the research.
- Visualisation is a key component of GIS. Through this study, we can conclude that colour coded maps can be created from the attribute information attached to the map. Apart from that variation based on land values, rateable values, and actual rates can be mapped using logical operators in GIS.
- Having this information available through clicking a few buttons can tremendously change the way rates are managed at LCC and greatly improve on effectiveness in revenue collection as shown in the results.

5.3 Recommendations

Based on the results obtained during this research, the following recommendations have been deduced:

- A corporate approach should be taken with regard to GIS implementation in Lusaka City Council as it will enhance the integration of council processes and systems, improve information management within the council, and create a knowledge management culture. This database will go beyond rates data, it will incorporate all the spatially based data such as billboards, booths, current planning developments such as subdivisions and this information can aid to tracking all activities and also monitoring trends in land development. The central server can be used to store all the data that is available and can be accessed by the various departments. This will allow for easy sharing of any available data (whether spatial or non-spatial) amongst the various departments and this in turn will have a positive effect on the process of rates collection for the organisation and collection of revenue on a larger scale.
- Lusaka City Council must invest in data collection tools and technology to aid in the process of constantly updating their existing spatial database. This entails scanning and conversion of all existing hardcopy data to be used as a base for adding new or current data, purchasing of spatial data collection devices such as Global Navigation Satellite System (GNSS) equipment or portable and handheld devices to collection points of interest on the ground.
- The Council should invest in up to date and accurate spatial information for example cadastral and aerial photography / images to provide effective decision support. This entails an easier flow of information between LCC and major stakeholders in there datasets such as the Ministry of Lands, Natural resources and Environmental Protection and the National Remote Sensing Centre.
- The Lusaka City Council should establish a programmatic regular training in GIS technologies and procedures in order to assign new roles where necessary. This will aid the organisation in keeping up-to-date with the constantly changing and improving technologies in the field of GIS.

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APPENDICES

APPENDIX A: ANCILLARY DATA

Appendix A 1: Semi-structured interview guide

Abbildung in dieser Leseprobe nicht enthalten

Source: Formulated by Author (2017)

Appendix A 2: Data Description

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Source: Formulated by Author (2017)

APPENDIX B: MAPS

Editorial note: Appendix B 1 was removed due to editorial reasons.

Appendix B 1: Hard copy maps used at LCC and storage drawers Source: LCC (2017)

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Appendix B 2: Parcel-based Study area Map without Improvement Footprints Source: Formulated by Author (2017)

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Appendix B 3: Parcel-based Study area map with Improvement Footprints Source: Formulated by Author (2017)