Master Thesis
submitted within the UNIGIS MSc programme at the Centre for GeoInformatics (Z_GIS)
Salzburg University
Enabling Spatial Data Management and Implementation in Namibia
By
Mr. Lisho Christoh Mundia
STUDENT NUMBER: 432476
A thesis submitted in partial fulfilment of the requirements of the Degree of
Master of Science (Geographical Information Science & Systems) – MSc (GISc) Advisor:
Mr. Nikolaus Strobel
Windhoek, Namibia, November 2009
This is to certify that the work is entirely my own and not of any other person, unless explicitly acknowledged (including citation of published and unpublished sources). The work has not previously been submitted in any form to Salzburg University or any other institution for assessment for any other purpose.
Signed: Lisho Christoh Mundia Date: 13-November-2009
The results presented in this thesis are based on my own research in the Centre for GeoInformatics (Z_GIS), Salzburg University. All assistance received from other individuals and organisations has been acknowledged and full reference is made to all published and unpublished sources used.
Special thanks go to Elma Tholiso – Mundia, my wife and my son Lisho Mundia Junior for their devoted support and encouraging attitude during my studies. My undivided and faithful appreciation also goes to my friend and a mentor – Faniel Maanda who has always been a supportive friend in all my studies. A big thank you also goes to those that are reading and using this thesis in any way, thank you for your time and for reviewing my work.
This thesis has not been submitted previously for a degree at any Institution.
DECLARATIONS ... I ACKNOWLEDGEMENTS ... II TABLE OF CONTENTS ... III
List of Figures ... v
ABSTRACT ... VI CHAPTER 1: INTRODUCTION ... 1
1.1 Introduction ... 1
1.1.1 Why the Study ... 1
1.1.2 Description of the Study Area ... 1
1.2 Problem statement ... 3
1.3 Aim and Objectives of the Study ... 4
1.4 Research Questions ... 4
1.5 Hypothesis ... 4
1.6 Scope and Limitations of the Study ... 5
1.7 Summary of the thesis ... 5
CHAPTER 2: LITERATURE REVIEW ... 6
2.1 Introduction ... 6
2.2 GIS and Spatial Data Management in Namibia ... 6
2.2.1 GIS and Spatial Data Management ... 6
2.2.2 General Spatial Data Management in Namibia ... 8
2.2.3 GIS and Statistics Development in Namibia ... 10
2.2.4 Namibia Spatial Data Infrastructure (NSDI) ... 11
2.3 Tools and Resources for Spatial Data Management ... 12
2.3.1 DBMS for Spatial Data Management ... 12
2.3.2 ICT and GIS in Spatial Data Management ... 14
2.3.3 WebGIS, OpenGIS and Distributed GI Infrastructures in Spatial Data Management ... 15
2.3.4 Spatial Data of Namibia ... 17
2.4 Data Sources, Input & Editing and Quality Issues ... 18
2.4.1 Spatial Data Sources ... 18
2.4.2 Spatial Data Input and Editing ... 19
2.4.3 Spatial Data Quality Issues ... 21
2.5 Summary of the Chapter ... 21
CHAPTER 3: MATERIALS AND METHODS ... 23
3.1 Introduction ... 23
3.2 Theoretical Foundation of Materials and Methods ... 23
3.3 Data Collection Method ... 24
3.3.1 Research Questionnaire ... 24
3.3.2 Context of the Research Questionnaire ... 26
3.4 Summary of the Chapter ... 27
CHAPTER 4: RESEARCH FINDINGS AND ANALYSIS ... 28
4.1 Introduction ... 28
4.2 Questionnaire Results ... 28
4.2.1 Organisation Information ... 28
4.2.2 Spatial Data, GIS Software, Management and Accessibility ... 28
4.2.3 Spatial Data Sources, Reliability ... 32
4.2.4 People and Training ... 35
4.3 Analysis of Research Findings, Situational Findings and Analysis of GIS and IT Infrastructures ... 37
4.4 Summary of the Chapter ... 41
CHAPTER 5: DISCUSSION AND CONCLUSION ... 42
5.1 Introduction ... 42
5.2 Discussion of Results ... 42
5.3 Conclusion ... 44
5.4 Future Work ... 45
GLOSSARY ... 46
BIBLIOGRAPHY ... 48
APPENDICES ... I
Appendix 1: Research Questionnaire ... iAppendix 2: Other comments from the respondents ... v
Appendix 3: GIS Organisations with GIS Activities and Their Level of Operations ... v
List of Figures
Figure 1: Study area map ... 2Figure 2: Components of GIS (Source: Adopted from Petch, 1999, p.1) ... 7
Figure 3: Sharing geospatial data (Source: UNIGIS, 2006) ... 16
Figure 4: The data stream (Heywood et al. 1998, p90) ... 20
Figure 5: Questionnaire contents and relationships ... 26
Figure 6: Spatial analysis Software mostly used in Namibia ... 29
Figure 7: Software usage scale level organisations ... 30
Figure 8: Spatial database engine and database management systems ... 30
Figure 9: Spatial data acquisition and sharing methods ... 31
Figure 10: Factors affecting the development of GIS systems ... 32
Figure 11: Spatial data providers’ in Namibia ... 33
Figure 12: Spatial data quality standard ... 34
Figure 13: Quality control methods ... 34
Figure 14: Spatial data quality standard ... 35
Figure 15: Fulltime GIS staff member vs. no GIS specialist ... 36
Figure 16: Staff members allocated GIS tasks ... 36
Figure 17: Organisation support to GIS training courses level ... 37
Figure 19: GIS strategic plan life cycle' six phases (Adopted from ESRI, 2009) ... 39
Namibia, like any other developing country in Africa faces a situation whereby the importance and implementation of spatial data management technology is still unclear. Many organisations such as key Governmental institutions, private organisations and private state owned organisations lack secure data, reliable access to centrally managed information for their organizations and that of their stakeholders at national level. According to Makanga, Smit and Paradzayi (2008) spatial data is a key resource for the development of a nation. There is a lot of economic potential that is locked away in spatial data collections and this potential is realized by making the data widely available.
With the above concerns; this research investigated how spatial data management is been handled in Namibia, a standard questionnaire attached to this report as Appendix 1 was used to solicit the views of different users. The results clearly bring together the level of understanding on how important spatial data management and sources are in Namibia. Furthermore, the role of organisations in spatial data management, including how their data are centralized and at what level in the organisation was also made clear. The most important part of the result is the recommendation of the strategic plan compiled for implementation when introducing a GIS system in an organisation. The strategic plan clearly indicates stages that all require special attention during the project.
This study is of benefit to any ministry of the Namibian government; private state owned companies, private companies, higher institutions, individually owned companies, and even originations in other parts of the world who utilize Geographical Information Systems (GIS) technology and so are custodians of spatial data.
1.1 Introduction
This chapter presents the reasons for doing this study. It further provides the description of the study area, aim and objectives of the study, research questions to be answered and hypothesis of the study. In its last section, it presents the scope and limitation of the study, thesis structure and finally the summary of this chapter.
1.1.1 Why the Study
Re-collecting of spatial data, reuse and re-sharing has become the norm and has resulted in some organisations depending on secondary data. Although this is right in the context of spatial data sharing and sources, there is great concern over the lack of proper spatial data management and implementation in Namibia. Many organisations share/acquire data without knowing the consistency and quality of these data. No proper mechanisms are put in place to monitor and check the consistency of these data.
With the above concern and the fact that spatial data is a key resource for the development of a nation, it is necessary to stress the importance of spatial data management and implementation in Namibia to avoid disastrous decisions been made with unsecured and poor quality data. With the ongoing improvement of GIS technology, it is possible to share the cost of one secure Database Management System (DBMS) by two or more organisations for efficient spatial data management, integration and sharing. This study therefore was necessary to find solution to the above concerns.
1.1.2 Description of the Study Area
The Republic of Namibia is a vast, sparsely populated country situated along the south Atlantic coast of Africa between 17 and 29 degrees south of the Equator. With its surface area of 824 268 square kilometres, Namibia is the 31st largest country in the world (Republic of Namibia, 2007).
Figure 1: Study area map
It shares borders with Angola and Zambia to the north, Botswana to the east, and South Africa to the south and southeast. It gained independence from South Africa on 21 March 1990 following
the Namibian War of Independence. The capital city is Windhoek. Namibia is a member state of the United Nations (UN), the Southern African Development Community (SADC), the African Union (AU), and the Commonwealth of Nations (Wikipedia, 2009).
The country is demarcated into 13 regions (see figure 1 above), namely the Caprivi, Kavango, Kunene, Omusati, Ohangwena, Oshana and Oshikoto regions in the north, the Omaheke.
Otjozondjupa, Erongo and Khomas regions can be seen in the central areas of the country, whilst the Hardap and Karas regions are in the south.
Namibia’s population is 2,088,669 million and the current growth rate is 2.6 percent for the 2007 – 2008 projection. According to the 2001 population census the country has a relatively youthful population with 39% of the population under 15 years of age and only 7% over 60. Despite rapid urbanization, Namibia is still a mainly rural society with 33% of the population living in urban areas. Regional population densities vary enormously with almost two-thirds of the population living in four of the northern regions and less than one tenth of the population living in the south.
1.2 Problem statement
No research has been carried out in Namibia for “Enabling Spatial Data Management and Implementation in Namibia”. Although relevant literature such as national cadastre, National Land Information Systems (NLIS) exists, research on the importance of spatial data management using spatial DBMS, WebGIS, etc has not been stressed in the country before.
What is the importance of spatial data management at national, regional and local level in Namibia? This is one of the main questions to be answered by this study. This includes examining the current methods and techniques used in different organisations to manage spatial data as well as determining the role and the need for spatial data management tools at national level in handling all types of spatial data by developing a strategic plan model.
The theoretical framework for this research will be the current debate on the spatial data management technologies, GIS DBMS technologies, Information Communication Technology (ICT), and Spatial Data Engine (SDE) & Structured Query Language (SQL) and OpenGIS in integrating and managing spatial data to centrally managed information for Namibia. The sources of spatial data in the country will be reviewed.
1.3 Aim and Objectives of the Study
The main focus of this study is to attempt to make clear the role and importance of spatial data management, the major sources of spatial data and understanding of centralised data. Whilst, the objectives are to:
Have clear scientific understanding and clearly outline the importance of spatial data management, major spatial data sources and implementations,
Complete a spatial data management strategic plan for the entire country, including companies that have spatial data,
Critically analyze levels of understanding of spatial data management and implementation in Namibia from questionnaire results i.e. list of institutions that manage their own data, etc and at what scale, and
Represent by means of maps, graphs and tables depicting institutions all over Namibia that can play a role or have played a role in spatial data management at national level, and strategies on how to influence spatial data management techniques and technologies available.
1.4 Research Questions
This research intends to answer the following questions:
What is the importance of spatial data and its security in Namibia?
How is spatial data collected and shared from one organisation to another?
What are the reliable sources of spatial data and are these data secured?
What are the roles, benefits, efficiency and need of Spatial Database Engine (SDE), web mapping and other available technologies, in handlings spatial data for organisations and at national level? and
What are the Database Management Systems (DBMS) and WebGIS technologies used in Namibia to manage and store spatial data and to what degree are they implemented?
1.5 Hypothesis
To fulfil the above objectives, the following hypothesis guides this research:
The existing spatial data management system in Namibia is good enough and does not require much effort to improve the system although continuous systems improvement is highly recommended.
GIS spatial data management techniques are increasingly more user- friendly and comprehensive. In Namibia, the reason for struggles in the implementation phase can be ignorance of spatial data. In addition, there is a lack of availability of the needed resources to properly manage spatial data.
The successful implementation of GIS requires professional knowledge and skills for a developing country like Namibia.
For the country to increase experience and enthusiasm in GIS management and development, the (potential) users need good training and education.
1.6 Scope and Limitations of the Study
This research will look at “Enabling spatial data management in Namibia” and relate how spatial data, data quality control, sharing, access and integration can be improved by implementing strategies that support the spatial data management process. The core concern of the research is on how to enable spatial data management in the organisation and understand the importance of this aspect. In cognisance of the above, this research limits itself to the evaluation of the potential of spatial data management, access, sharing, quality and integration.
1.7 Summary of the thesis
Chapter 1 presents an introduction and background to the research, and the main purpose of this research. Chapter 2 is a literature review and looks at how spatial data management is carried out in Namibia. It proceeds with an overview of spatial data management, databases issues, data sources and integration. Chapter 3 concerns research methodology and looks at the materials and methods used for the research. It describes the sources of data used in the research as well as an indication on the research design and strategy that was adopted.
Chapter 4 presents the findings of the study and presents specific proposals on how spatial data management implementation can be carried and in Namibia. Specific emphasis is placed on spatial data management strategies for various levels of operations. Chapter 5 presents the conclusion and recommendations for this study.
Chapter 2: Literature Review 2.1 Introduction
This chapter defines, describes and explains the relevant literature on spatial data management and implementation in Namibia. Major keys words of this study, including various related literatures are defined and linked. The chapter scientifically explains, discusses, distinguishes and differentiates the relationship of all relevant spatial data management tools.
It starts by looking at the scope of GIS and spatial data management in Namibia, where GIS and spatial data management are defined, and broadly looks into the context of spatial data management and GIS in Namibia. In depth it looks into different major organisations that have contributed or plays a role in spatial data management. It further looks at DBMS software1, including ICT and spatial data management activities in context. In its last sections, it reviews the level of WebGIS and OpenGIS distributed GI infrastructure, the base datasets required management and data quality issues.
2.2 GIS and Spatial Data Management in Namibia
2.2.1 GIS and Spatial Data Management
Wade and Sommer (2006) defined GIS as an integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial process. A GIS provides a framework for gathering and organizing spatial data and related information so that it can be displayed and analyzed.
1 general name for computer programs and programming languages.
Figure 2: Components of GIS (Source: Adopted from Petch, 1999, p.1)
The term ‘Geographical Information Systems’ describes a technology, Geographical Information Science (GIS) is a modern term used meaning GIS is more than a system which can process much data. They offer functions for collecting, storing, processing, and retrieval of spatial data.
The definitions of GIS given above are expressed as a centre of the components of GIS (Figure 2) as a field of study. The field of GIS can be divided into five components (Petch, 1999, p.4):
Technology Data
Organizations Methods Body of ideas
GIS can be viewed as a software package, the components being the various tools used to enter, manipulate, analyse and output. In combining these components together, the output should be a successful GIS with different roles which should increase efficiency in an organisation when properly managed. As stated by Petch (1999, p.1), all information systems are based on data and rules for using data in some form or other. The main components of the data part of the system (figure 2) are inputs, spatial and other forms of databases, data maintenance systems and quality assurance systems. At this level GIS begins to include both technological entities as well as human systems.
The Bathurst Declaration defines spatial data as “data/information relating to the land, sea or air that can be referenced to a position on the earth’s surface” (Bathurst Declaration, 1999). Bathurst Declaration (1999) further explains that such data is “the key to planning, sustainable management and development of our natural resources at local, national, regional and global levels”. Spatial data management refers to the effort to create, update and maintain spatial data with its attributes and its associated metadata to accurately reflect the organisation and national federal owned inventory.
2.2.2 General Spatial Data Management in Namibia
In Namibia, considerable amount of work in spatial data management aspect have been achieved at departmental and organizational level; a lot still has to be done at National level to enable GIS users in remote areas ability to access complete, secured and updated spatial data. As indicated by Noongo (2003), “GIS have facilitated the production and generation of digital data in various government agencies tasked with planning, managing and monitoring of natural resources. In most cases data that are collected for particular project are useful for other projects”.
According to Letsie (2005, p.3) “Spatial data are critical to promote economic development, improve our stewardship of natural resources and to protect the environment. Spatial data forms the basis in decision making by the government and the business community for proper spatial analysis”. Generally, a complete spatial data management system in any organisation will have a database, data, data network, users, policies and standards and the GIS application to fully apply the data. Wade and Sommer (2006, p.48) define a database as “one or more structured sets of persisted data, managed and stored as a unit and generally associated with software to update and query the data. A simple database might be a single file with many records, each of which references the same set of fields. A GIS database includes data about the spatial locations and shapes of geographic features recorded as points, lines, areas, pixels, grid cells, or triangulated irregular networks (TINs), as well as their attributes”. According to Worboys (2001, p.17) “a database is a store of interrelated data to be shared by a group of users. Items of data may be numbers, character strings, text, images, sounds, spatial configures; in fact anything that can be measured, recorded and to which it is possible to assign meaning”.
Noongo (2003) stated that “much of the data in Namibia take the form of maps and paper records.
There has nevertheless been a realization that the use of computers in data management necessitates availability of such data in digital formats. Consequently, efforts are still underway to create digital databases in many agencies through the conversion of existing maps and paper records into digital format. The current progress in database development shows that various agencies are implementing components of data infrastructure to better manage and utilize their datasets”.
The Environmental Information Systems Unit of the Ministry of Environment and Tourism has in the past 10 years created the “National Metadata Directory” and has also created and registered a metadata clearinghouse node for Namibia at the global Metadata Clearinghouse. Proper documentation that could answer questions regarding the content, quality, accessibility, and other characteristics of spatial data enforces the need for good metadata (Woldai, 2003). A metadata webpage, http://www.met.gov.na/publications/Databases/MetaDB/metadataEnv.htm was created in 2003. The webpage includes introductory information on metadata, its use and management. The webpage contains over 500 metadata records from various agencies involved in data collection and usage. The Metadata Clearinghouse, where metadata could be searched and viewed at international level could be accessible at the following webpage also - http://clearinghouse4.fgdc.gov/registry/clearinghouse_sites.html. The global Clearinghouse activity sponsored by the Federal Geographic Data Committee (FGDC) is a decentralized system of servers accessible through the internet which contain field-level descriptions of available digital spatial data. This Clearinghouse allows individual agencies, consortia, or geographically-defined communities to band together and promote their available data.
The national “Atlas of Namibia” and a digital “Atlas Database” were completed towards the end of 2002. The Atlas provides basic reference material on the geography of Namibia, including social, demographic, economic, infrastructural, physical, climatic and biological features of the country.
All datasets are accompanied by a metadata. Most of the data are in ArcView format and in zipped
files. The data is downloadable from http://www.uni-
koeln.de/sfb389/e/e1/download/atlas_namibia/main_namibia_atlas.html. There are still improvements required to be done on the current national Atlas of Namibia. The data are as yet still incomplete. One cannot use the shapefile for pipelines to indicate the type of pipeline been
viewed. The roads are still very outdated, for example, the trunk road does not connect/cover the Caprivi region which is a thoroughfare to four bordering countries (Zambia, Zimbabwe, Botswana and Angola).
2.2.3 GIS and Statistics Development in Namibia
The Government of Namibia formulated an ambitious vision that by 2030 Namibia should join the ranks of high-income countries and afford all its citizens a quality of life that is comparable to that of the developed world. It calls for intended rapid economic growth to be accompanied by equitable social development. The tools of implementation are five-year National Development Plans (NDPs).
According to the National Planning Commission (2008) the main development objective of deploying GIS and Statistics was to contribute to the development of a knowledge-based economy and technology-driven nation through the enhancement of GIS and statistics managed by the Office of the President under the National Planning Commission (NPC) Secretariat. The specific objective was to improve production, accessibility and distribution of geospatial and statistical information. The 2001 census used GIS to create digital maps to facilitate the data collection process (Mwazi, 2007, p.3). The project titled “GIS and Statistics Development in Namibia” was realized in August 2007 with the fund agreement between the Central Bureau of Statistics (CBS) in the National Planning Commission (NPC) and the Development Cooperation of the Grand Duchy of Luxembourg. A number of sub project are to be realized, including a newly introduced Bachelor of Geo-Information Technology Degree offered by the Polytechnic of Namibia.
The development of GIS and statistics in Namibia fits in the Vision 2030 goal of creating knowledge based economy. The government made a commitment to improve access to geo-spatial information and statistics by the year 2011.The Geo-spatial Information and Statistics sub-sector of the NDPs is composed of the two broad inter-related areas; Geo-spatial Information and Official Statistics. The productions and management is common to both. Both spatial, statistical and other attribute data and information are seen as providing the bedrock for e-government2.
2 is the use of Information and Communication Technologies (ICTs) to improve the activities of public sector organisations.
2.2.4 Namibia Spatial Data Infrastructure (NSDI)
NSDI is thus a national initiative to provide better access for all Namibians to essential spatial data.
It aims to ensure that users of spatial data will be able to acquire consistent datasets to meet their requirements, even though the data is collected and maintained by different authorities.
According to Noongo (2003) the “Environmental Monitoring and Indicator Network (EMIN) in June 2002, realized the need to establish a National Spatial Data Infrastructure (NSDI) for promoting greater awareness and public access to standard and coordinated spatial data production, management and dissemination by all sector agencies including the establishment of a Spatial Data Clearinghouse at national level”. The concept of NSDI is not to establish a central database, but to set up a widespread decentralized network of databases, managed by individual government and industry custodians. According to Woldai, (2003), “government at all levels (local, provincial, national) requires unrestricted and efficient access to reliable, timely, up to date fundamental geo-information to govern”.
There is an increasing awareness in the public and private sectors of the importance of spatial data in managing the country’s resources. It is therefore not surprising that many agencies in charge of monitoring and management of resources have developed their own capacity to use GIS tools.
Equipment and software are purchased and in most cases spatial data is used by thematic experts (conservationists, land surveyors, engineers, town planners, hydrologists, geologists, etc), and not by experts in GIS. According to (Noongo, 2003) some of the benefits offered by NSDI to Namibians are:
Enhancing the sharing and open access to data by different users for a variety of environmental, natural resource management and development planning applications.
Enhancing the scope of efficient use of human and natural resources in the country while making the distribution of data and social dimension associated with data access more transparent.
Increasing knowledge about the country’s natural resources thereby increasing the chances of investment.
Increasing the general level of knowledge and access to information within Namibian society, (schools, communities, organizations and decision-makers) and thereby stimulate economic growth and democratic participation in national and local processes.
With the NSDI in place, new development, monitoring and poverty alleviation programs can more effectively target problem areas using data and analysis tools without having to begin the program with extensive data collection schemes.
NSDI can help provide the foundation for badly needed monitoring programs (for environmental, economic and social changes) in a more cost-effective and consistent manner.
A co-ordinated approach to building Spatial Data Infrastructure (SDI)3 is motivated both by the fact that it is cost-effective, as well as necessary to ensure that information from diverse sources can be integrated readily and meaningfully (Gavin, 2002, p.1). Despite the awareness on importance of the data, the current situation in Namibia is that there is minimal coordination of spatial data in the GIS community. Software, data structure, projection systems, procedures and data documentations are not coordinated. Most data produced by government agencies are either not documented or poorly documented. The same data are often produced a few times by different agencies.
2.3 Tools and Resources for Spatial Data Management
2.3.1 DBMS for Spatial Data Management
Managing spatial data require the right tools, and the right tools require data. The great importance of all is that data has to be efficiently accessed and secured; this is all done by using either one of the various available tools on the market, such as Database Management System (DBMS). The http://dbms.ca [online] defined a DBMS as system software used to manage the organization, storage, access, security and integrity of data in a structured database. The nature of database management systems has dramatically changed since 1960 as the demand for data storage has increased and the technology to store data. A database can be a set of flat files stored on computer tape or disk or could consist of database tables that are managed by a DBMS. There are different types of DBMS products; relational, network and hierarchical. The most extensively
3 Spatial Data Infrastructure (SDI) is often used to denote the relevant base collection of technologies, policies and institutional arrangements that facilitate the availability of and access to spatial data (Global Spatial Data Infrastructure, 2004, p.8)
used type of DBMS today is the Relational Database Management Systems (RDBMS)4. Worboys (2001, p.21) stated that a centralised database system has the property that data, DBMS and hardware are all gathered together in a single site, that does not preclude remote access from terminals away from the computer system.
Some DBMS can be accessed directly using programming languages such as COBOL while others provide their own programming language for interacting with the database. Many DBMS applications also provide reporting and query tools to view data in the database. The early DBMS systems required that data be structured in a manner that was conducive to how it would be stored and/or accessed. Data was stored in database records that were linked to related data via
"pointers". Although access speed was good, flexible access to data was not. As the cost of data storage fell, it became feasible to store data in tables. This eliminated much data redundancy and provided much more flexible data access (http://dbms.ca [online]). Here are some useful roles played by the DBMS in spatial data management;
It maintains the data definitions for each table and columns in the database. Each piece of data must be assigned a name, a data type (e.g. date, alphanumeric, numeric) and a mandatory/optional status.
DBMS packages can also enforce domain rules. For example, a domain for marital status, state codes or country codes could be defined to ensure that only valid values are stored.
Another important role of a DBMS is to enforce data security. Based on the assigned roles of users, a DBMS system can ensure that a given user only has read or write access to appropriate columns in the database. This ensures that respective data is only accessible to the appropriate users. Data access can be restricted via database "views" that filter out sensitive data and by other means.
Many DBMS applications can track changes made to tables in the database. Along with the prior version of the data, the DBMS will record the identity of the person who altered the data. Maintaining audit trails on important data is another important role for DBMS.
Some of the available DBMS today includes DB2, Microsoft Office Access, MySQL, Oracle and Microsoft's SQL Server DBMS.
4 any database management systems that can access a relational database, generate responses to ad hoc queries and preformatted reports by using relational algebra or calculus, and perform routine update operations.
2.3.2 ICT and GIS in Spatial Data Management
GIS is a branch of Information Communication Technology (ICT) that form part of the tools for good governance. ICT and GIS are advancing rapidly. New hardware architectures are emerging, such as blade servers Grid computing, where the computing resources of several computers on a high speed network can be combined to offer faster data and computations. New approaches to the delivery of applications are emerging in the form of Service Oriented Architecture (SOA), whereby data can reside on one server, and applications on that data reside on another server, and both can be accessed from something as simple as a standard web browser. All in all, data and transaction is greatly increased with faster core technology, as well as new computing and storage architectures.
According to Daugherty (n.d) information flow is increasing; more and more of the natural and manmade processes of the world are being measured in real time using sensors from space, or telemetry from ground based systems and sensors. The data collected is amassed in very large databases, accessible by various applications, and delivered as a service. This improved information flow is as good for government administrators as it is for our private use; improving efficiency, communications, collaboration, management and decision making.
GIS combine technology, science and a methodology that enables us to study the complex nature of our world. We have increasing population, urbanization, land degradation and coastal development. We have environmental changes which arguably are caused by a decline in our natural ecosystems. There is advancing science and knowledge and information flow around the world is nearly instantaneous. As a result, our world is more complex, and the problems of our global society are challenging. To understand, to analyze, to plan, and to make decisions of these aspects of changing world, we need geographic knowledge (http://www.itc.nl/ [online]).
Finally, as we introduce and implement GIS and ICT into developing countries like Namibia, we need to understand that each country is different.Success comes from simple steps and exercises using practical applications and local data. As important as technology transfer is the building sense of community and purpose for all members of the organization.
2.3.3 WebGIS, OpenGIS and Distributed GI Infrastructures in Spatial Data Management
The Indian Institute of Information Technology
and Management-Kerala – (IIITM-K) (2009) stated that the synergy of GIS and Web technology allows access to dynamic geospatial information without burdening the users with complicated and expensive software (http://www.edugrid.ac.in [online]). The World Wide Web (www) provides GIS users easy access to spatial data in a distributed environment through a simple browser interface or sometimes by a lightweight client side application. The concept of Web GIS is based on how the map is produced and responds to users' interactions over the Web. The publication and distribution of spatial data are increasingly important activities enabling organizations to share domain-specific dynamic spatial information over the Web. WebGIS add GIS functionality to a wide range of internet-based applications in government, business, research and education. It has several advantages such as worldwide access, dynamic data access and user- friendly interface (http://www.edugrid.ac.in [online]).
GIS Lounge and DM Geographics (2008) defined OpenGIS as the full integration of geospatial data into mainstream information technology. What this means is that GIS users would be able to freely exchange data over a range of GIS software systems and networks without having to worry about format conversion or proprietary data types.
In a true ‘OpenGIS’, agencies who operate more than one mapping system can interchange graphic and attribute data with other popular GIS systems. The intent is to move away from the current status quo in which specific GIS applications and capabilities are tightly coupled to their internal data models and structures. In other words, utilizing an ESRI based software package also requires ESRI proprietary data structure such as shapefiles or ArcInfo coverages in order to utilize all analytical aspects of the software. Currently, a user who wishes to gain access to geodata developed by another agency is generally faced with a complex data conversion task (http://gislounge.com [online]). Figure 3 below show the integration framework of spatial data exchange and relationships to information dissemination. As quoted from Open Geospatial Consortium (2009), “Open Geospatial Consortium (OGC) is a non-profit, international, voluntary consensus standards organization that is leading the development of standards for geospatial and location based services”.
Figure 3: Sharing geospatial data (Source: UNIGIS, 2006)
OGC is a consortium of over 365 companies, agencies and universities working toward a world in which everyone benefits from geographic information and services made available across any network application, or platform. The mission of the OGC is to promote the development and use of advanced open systems standards and techniques in the area of geoprocessing and related information technologies. OGC is supported by Consortium membership fees and, to a lesser extent, development partnerships and publicly funded cooperative programs (http://www.opengeospatial.org/ [online]).
The Open Geospatial Consortium (2009) further states that it manages a global consensus process that results in approved interface and encoding specifications that enable interoperability among and between diverse geospatial data stores, services, and applications. In the OGC, geospatial technology users work with technology providers. OGC standards provide essential infrastructure for the Spatial Web, a network of geospatial resources that is thoroughly integrated into Web.
2.3.4 Spatial Data of Namibia
Geo-information is vital for improving the economic productivity of a country’s human and natural resources (Woldai, 2003, p.1). Spatial data describe and identify the geographic location and properties of natural or administrative features on the earth. Spatial data itself consist of three parts, namely the information on the location of the feature, the physical or logical properties of the feature (attribute data) and the administrative description of the data set (metadata). Table 1 below provides the features and datasets commonly used in Namibia.
Table 1: Base datasets of Namibia
Features Datasets
Administrative Boundaries
National Boundary, Regional Boundaries District Boundaries, Constituency Boundaries and National Park Boundaries
Infrastructure
Towns, Roads, Railway Lines, Air Strips, Power Lines, Telecommunication lines, Mines, Health facilities, Malaria infection, HIV infection, Education facilities and
Judicial services
Land Local government, Land ownership, Land control,
Conservation, Land Usage, Land Cover and Land forms.
Population Population distribution, Population density, Population in urban & rural areas and Language groups and major dialects
Surface Water Features Rivers (first order), Lakes, Dams and Surface Water Supply Schemes
Groundwater Water Features Aquifer Potential, Groundwater Supply Schemes and Groundwater Control Areas
Environmental Information
Geology, Soil, Temperature, Rainfall, Evaporation, Wind And Vegetation
2.4 Data Sources, Input & Editing and Quality Issues
2.4.1 Spatial Data Sources
There are a number of sources of spatial and attribute data, including maps, census and survey data, aerial photographs, satellite images, soft data and Global Positioning Systems (GPS) data, which have additional special characteristics into the GIS areas, whilst most of this data sources are generally used in Namibia to collect spatial data. Spatial data are costly and most organisations have limited resources available to collect data. Data may take time to collect; yet we have deadlines to meet. In building a GIS we need to become skilful at finding and obtaining data. We often need to be both imaginative and flexible in the sources we use. As Longley, et al., (2001, p.1) has said “one of the main reasons for the success of GIS has been their ability to bring together different data sets. It is this which allows them to generate new levels of information, and thus to provide new insight. It is this, which provides the ‘added value’ from using GIS”.
Currently, satellite navigation systems or survey data are the main sources of the cadastral data, land use planning (urban-rural), and remote sensing5. The collection of survey data is costly and time-consuming but it is very accurate in locating survey points on the ground, this mostly is done using Global Positioning Systems (GPS). In recent years, GPS have become relatively cheaper and affordable by many, which is why is the most favourable to the use in spatial data collection and management. The technology is nevertheless not entirely perfect, and positional errors may occur for a number of reasons, including errors in the satellite clock, the receiver, atmospheric interference, human errors (setting on a wrong control point or typing wrong coordinates) and signal reflection from features on the ground.
Maps and remotely sensed imagery usually satisfy most of the organisations’ needs in spatial data.
There are relatively limited sources of the attribute data which are needed for GIS. Some attribute information may be provided in the map legends or accompanying records, some may be derived from the map symbols and text e.g. contour heights, place names, etc (Longley, et al., 2001, p.1).
The most usable one in upgrading development when designing layout plans in town or regional planning is the aerial photographs – like maps - are analogue documents. Other sources are sometimes used but on a very limited scale, this is mainly due to poor reliability of results.
5 the act of detection or identification of an object without having the sensor in direct contact with the object; includes satellite imagery and aerial photography.
2.4.2 Spatial Data Input and Editing
Normally, data would be entered into a GIS before being structured. Without data a GIS will not produce output. Spatial data can be obtained from many different sources in different formats, and can be input to GIS using a number of different methods, see figure 4 below for a data stream.
Data in analogue or digital format need to be encoded to be compatible with the GIS being used.
All data in analogue form need to be converted to digital form before they can be input into GIS.
Four methods are widely used: keyboard entry, manual digitizing, automatic digitizing and scanning.
Keyboard entry may be appropriate for tabular data, or for small numbers of co-ordinate pairs read from a paper map source or pocket GPS. Digitizing is widely used for the encoding of paper maps and data from interpreted air photographs. Scanning represents a faster encoding method for the data sources, digital data may require considerable time before analysis is possible. Spatial data may be collected in digital form and transferred from the devices such as GPS receivers, total stations (electronic distance-metering theodolites), and data loggers attached to all manner of scientific monitoring equipment; this process is called electronic data transfer.
During encoding a range of errors can be introduced. Whilst keyboard encoding, it is easy for an operator to make a typing mistake; during digitizing an operator may encode the wrong lines; and folds and strains can easily be scanned and mistaken for real geographical features, conversion of data between formats required by different packages may lead to loss of data. Errors in attribute data are relatively easy to spot and may be identified using manual comparison with the original data. Errors in spatial data are often more difficult to identify and correct than errors in attribute data. These errors take many forms, depending on the data model (vector or raster) and the method of data capture.
Data derived from maps drawn on different projections will need to be converted to a common projection system before they can be combined or analysed. If not re-projected (figure 4 illustrates) data derived from a source map drawn using one projection will not plot in the same
Maps Satellite Digital Data Tabular Data Soft Data
Digitizing Scanning Data Transfer Key coding
Data Capture
Integrated GIS database Editing/Cleaning
Re-projection
Generalization
Edge matching and rubber sheeting
Layering
Figure 4: The data stream (Heywood et al. 1998, p90)
location as data derived from another source map using a different projection system. When a study area extends across two or more map sheets small differences or mismatches between adjacent map sheets may need to be resolved. Normally, each map sheet would be digitized separately and then the adjacent sheets joined after editing, re-projection, transformation and generalization.
2.4.3 Spatial Data Quality Issues
The computing adage "Garbage In, Garbage Out" (GIGO) recognizes that if you put poor quality data into your program, you get poor quality results. This adage applies to GIS as well because the results of an analysis are only as good as the data put into the GIS in the first place. Error can be introduced at every stage of the data stream. Sources of errors are however not only limited to the process of data capturing and integration, exclusive to conceptual reality stage (Heywood, et al., 1998). If an error is already present in the source data and further errors arise during manipulation, output and use of the data in a GIS will both have errors.
There are varieties of errors in GIS; among others, the most common GIS errors are positional and attribute error. Other types of error are important but will usually be manifested in either of the two main types. According to Redman (1996, p.1) the case for improving data quality is pervasive, poor data quality is costly and data quality can be improved. It should therefore be remembered that errors in the application can entirely be limited and that all spatial data are subject to errors.
Errors propagate at all stages of GIS usage. We carefully need to consider problems associated with error at all stages of GIS usage if we are to maintain confidence in our output. Poor data quality impacts on the success of the organization, which is why it’s important to understand the need for spatial data management.
2.5 Summary of the Chapter
This chapter presented a glimpse into the need and differentiated roles played by spatial data management and GIS as a field of study. The statutory and non-statutory framework governing spatial data and statistical data in Namibia was discussed. The chapter looked at the important component of spatial data management and the roles played by various organisations in developing and upgrading their data management strategies.
The chapter expanded by looking at the potential of spatial data sources, input, editing and quality control for GIS. In the end it became clear that, spatial data management holds greater potential for planning and decision making in Namibia only if GIS experts and users knew how to implement and make full use of the technology.
3.1 Introduction
This chapter examines the methods and tools used in collecting data and sources of data for this study. It further presents the reasons for using and choosing the methods. The chapter begins by introducing the theoretical foundation of this study, details of the research questionnaire in the data collection method section is outlined, explained and discussed and lastly the tools and resources used as approaches contributed to the success of this study.
3.2 Theoretical Foundation of Materials and Methods
The theoretical foundation of this research was based on the current debate on spatial data management, the development on spatial data management and provision of geospatial data in Namibia and the world at large. The scientific articles and relevant books from Namibia and other countries were reviewed and used to support this study.
The main base of this research was the research questionnaire sent out to various organizations (government ministries, local authorities, etc) using GIS and those that provide GIS service in Namibia. The sample for this questionnaire includes all the implementation team from different GIS organisations (see appendix 3); these were selected based on the subject of this study
“Enabling Spatial Data Management and Implementation in Namibia” as mode of selection. The percentages of different organisations that received and completed the questionnaire are 37%
parastatals, 27% Government, 18% municipalities, 9% private companies and 9% GIS service providers.
The questionnaire aimed at gathering information on the level of GIS software, spatial data availability, data sources, management and accessibility of data, people and training. The research questionnaire therefore was to be completed only by organisations that have/use GIS and providing GIS services which then further determined the mode of selection of the questionnaire receiver. The feedback was then used to answer the targeted research objectives, questions and hypothesis set in chapter one above. Primary data and secondary data were used for this study and are explained below:
Primary data collection: involved the use of the questionnaire in order to collect information on the current state of spatial data availability, spatial analysis software, state of data, people and training in Namibia. The data were available in its raw form and therefore needed further analysis and interpretations in order to make a convincing conclusion on the need and importance of stressing the spatial data management in the country.
Secondary data collection: involved the vector spatial data, review of scientific literature (articles, reports, etc) on enabling the spatial data management in Namibia and other relevant literature on spatial data management. It further involved the spatial data to explore the use of GIS in Namibia and the importance of spatial data management.
Most part of the data types used in this study was in the forms of descriptive, statistical and combination of descriptive and experimental.
3.3 Data Collection Method
3.3.1 Research Questionnaire
The questionnaire research technique method was chosen to collect primary data on spatial data management in Namibia due to its suitability in gathering reasonable amount of data (covering major GIS stakeholders) about spatial data management, and because it is a quicker and cheaper method to use, given the timeframe for this research. Statistically, the data, inputs, comments, and information from enthusiastic organisations that have been viable in GIS activities in Namibia provided reasonable amount of data to determine the scale of spatial data management in Namibia. This is because the results of this study contain major stakeholders of GIS in Namibia and that the questionnaire respondents were above 50% of total number sent out.
According to Analytic Technologies (1997) “a questionnaire is a formal, written, set of closed and open-ended questions that are asked of every respondent in the study. The questions may be self- administered or interviewer-administered”. In summary of the above, a questionnaire is one of the instruments for data collection. Wikipedia (2009) defined a questionnaire as a “series of questions asked to individuals to obtain statistically useful information about a given topic. When properly constructed and responsibly administered, questionnaires become a vital instrument by
which statements can be made about specific groups of people or entire populations (http://en.wikipedia.org [online])”.
The types of questions used in the study includes: closed ended questions, multiple choice, and open ended questions. Closed ended questions is where respondents’ answers are limited to a fixed set of responses, this included the yes/no questions; whilst, multiple choice is where the respondent has several option from which to choose. Lastly, there was only one open ended questions in the questionnaire for this study where no options or predefined categories were suggested; this was where respondents were asked for any other comments. The respondent supplies their own answer without being constrained by a fixed set of possible responses.
On the 24th of March 2009, 35 copies of the research questionnaire were sent to different GIS users, experts and managers. The copies sent were based on the major GIS stakeholders’ contact list available, and gathered during this study. The respondents were expected to be forwarded to any GIS users, managers, experts, etc as indicated in the email sent containing the research questionnaire. This was purely to allow anonymous respondents to take part in the survey. The deadline for respondents to complete the questionnaires was 14th of April 2009. The time between March and October 2009, is relatively a long time. The discrepancy in time is of particular importance given the rapid take up of GIS. The target was to receive the minimum of twenty out of thirty five questionnaires back; the population target was set considering all the GIS major stakeholders in Namibia. The questionnaire was sent out via email, and the responses also sent back via email. The minimum of twenty questionnaires was enough and considered enough as an above the mean sample number of the thirty five sent questionnaire. It was also late accepted to be sufficient based on the fact that most organisations in Namibia have no GIS users within the organisation, they use consultants to manage their spatial data and do their GIS projects, (as appendix 3 indicates). Also, despite a number of decentralised programs in the country, GIS is still highly viable in the city (Windhoek) only.
A total of 23 completed questionnaires were returned. The questionnaires received include those from key GIS stakeholders, including: private, state owned organisations and government directorates that play a key role in spatial data management. Key GIS takeholders were indentified from SwedeSurvey AB feasibility study progress presentation which invited experts in GIS, land
surveying, environment, town planning, and many other experts from various ministries and organization held on the 28th of April 2006 to introduce the Namibian Land Information System (Mundia, 2007, p.25). Appendix 1 in the appendix section is the questionnaire used to gather information for this study on the spatial data status in Namibia.
3.3.2 Context of the Research Questionnaire
There were four main sections of the research questionnaire. The first part was the organisation information, followed by the spatial data, GIS software, management and accessibility section, the third and final was the spatial data sources, reliability and data types and lastly people and training section. Each aimed at gathering respective answers, below is Figure 5 showing the research questionnaire contents with their relationship.
Figure 5: Questionnaire contents and relationships
As figure 5’s section 1 shows, (organisation information) identifies the organisation by its name and location. The identification process is aimed at distinguishing respondents from organisations in Namibia from those working in organisations outside the country. This distinction is necessary as the sample target is organisations in Namibia. Section 2 is primarily on gathering information such as availability of spatial data in organisations, GIS software used in the organisation, and the level of use for the said software (departmental, organisation, section or division). The section further aimed at gathering information about database used, method of acquiring and sharing spatial data and lastly what factors holds the introduction of spatial database management tools in the organisation if any.
Section 3 focused mainly on spatial data sources and the reliability of data, mainly data in Namibia and knowledge about spatial data quality control standards. Lastly section 4 gave emphasis on people and training, including the availability of GIS specialists in the organisation, staff members allocated with GIS tasks and levels of GIS courses one can study in that organisation. Still in this section, people were requested to indicate whether they would like to receive the summary report of the research findings. All questions were interlinked to each other, and aimed at achieving the same objective; which is enabling the use of spatial data management and implementation in Namibia.
3.4 Summary of the Chapter
This chapter looked at the methods, tools and resources used to successfully complete this study.
The reasons and the importance of using the methods and materials applied were explained in this study. The chapter expanded by looking at the definition of a questionnaire as a primary means of collecting primary data, and further explained the need for resources such as people, computer, software, books and data and time and money as core of this study and how they all fit into the study methodology.
Chapter 4: Research Findings and Analysis
4.1 Introduction
In Chapter 2 it was mentioned that, DBMS and spatial data availability play vital roles in implementing a successful spatial data management in Namibia. In this chapter, beside reporting and analysing the need and importance of spatial data management, a further discussion of the results is included.
The extent and relative importance of the geographical data varies between projects, but taken together they present an opportunity to investigate how maps and geographical data can be presented on GIS in a variety of different contexts. GIS can integrate data from these different disciplines. The aim of this research was to contribute is to attempt to make clear the role and importance of spatial data management, the major sources of spatial data and understanding of centralised data”. The results are therefore presented.
4.2 Questionnaire Results
4.2.1 Organisation Information
The questionnaire result presented in this study were mainly from the major key government directorates, local authorities, private companies and state owned companies that has GIS responsibilities in spatial data collection, GIS usage, spatial data management and implementation.
Out of 22 received questionnaire, nineteen 19 came from Windhoek, the capital city of Namibia, in the Khomas region and only three came outside the city, from Otjiwarongo in the Otjozondjupa region, Figure 1 shows the map of Namibia, Although one cannot conclude the reasons for non- response by GIS users from outside the city despite sending the questionnaire into all parts of the country.
4.2.2 Spatial Data, GIS Software, Management and Accessibility
The questionnaire result of this study indicates that all organisations that responded have spatial data and spatial analysis software in their organisation. Figure 6 below shows the software mostly
used in Namibia in different organisations. This information also help to determine the most preferred spatial data format for exchange and even for building an integrated GIS system for Namibia.
Figure 6: Spatial analysis Software mostly used in Namibia
It should be noted here that respondents that have more than one software indicated all the software that are available or used in their organisation. From Figure 6 above, one can conclude that ArcGIS9x is commonly used in Namibia as it tops the list by 25% followed by AutoCAD with 10%, ArcView with 8%, Geomedia 6% and subsequently with ER Mapper, Global Mapper and IDRISI with each claiming 5%. Most software used in mining sectors dominated with 4%, this includes Geosoft, Rockware and Surfer. Interesting part to also notice here is the option “others”
which carry the 4%, this shows that there is software used in organisations which are not listed.
Given a number of GIS software listed in Figure 6 it was easier to establish whether the software mentioned is used by the entire organisation, the department, the division or other entities, e.g.
individuals. Figure 7 provides an answer to the above concern by illustrating the level at which spatial analyses software are used within organisations in Namibia.
Figure 7: Software usage scale level organisations
The results in figure 7 clearly show that most spatial analyses software are initiated or introduced at departmental level, this is seen with a score of 48%, followed by organisational spatial analyses software at 31% and subsequently with division/section level at 17% and 4% for others, which could be private.
In the survey people were asked: “Does your organisation own or have access to spatial analysis software?”, the questionnaire results revealed that 77% of the organisations have spatial database engine or a spatial database management system in place, while 23% has none at all.
Figure 8: Spatial database engine and database management systems
The results in Figure 8 shows that Geodatabases 6and Oracle spatial are the most used spatial DBMS with 26% in Namibia. The term geodatabase is defined as “a collection of geographic datasets for use by ArcGIS software. ArcSDE recorded 14%; PostGres and MapServer claimed 10%
each whilst PostGIS and MySQL both recorded 7%.
The results indicate that appropriate methods of data storage are used and that the importance and need for spatial data management in Namibia is understood. As shown in Figure 9 below, it is clear that in Namibia, spatial data is properly exchanged between producers and users using different methods and format.
Figure 9: Spatial data acquisition and sharing methods
As indicated in Figure 9 the exchanges of data either via shapefiles, Compact Discs (CDs) and emails are commonly dominant. These methods (CDs and emails) of data sharing are appropriate and show the need for spatial data management. It is also clear that some organisations are also allowed to exchange their data by plugging their external hard drives to their PCs; which shows
6 Wade and Sommer (2006) defined a geodatabase as “a collection of geographical datasets for use by ArcGIS. There are various types of geographical datasets, including feature classes, attribute tables, raster datasets, network datasets, topologies, and many others.
good interrelationship. Further to the above, it is evident that, Global Positioning System (GPS) and survey data are still widely used as sources for primary data.
Figure 10: Factors affecting the development of GIS systems
As shown in Figure 10, most organisations cite finance and lack of knowledge as major obstacles holding them back in implementing a spatial database management system, both scored up to 31% higher than the rest in this questionnaire category. This was followed by lack of interest with 22% and subsequently staff time with 14% and others with 2%.
4.2.3 Spatial Data Sources, Reliability
The questionnaire further revealed the main spatial data providers in Namibia. Figure 11 below shows the most spatial data provider in Namibia.
Figure 11: Spatial data providers’ in Namibia
Most companies are clearly relying on the Surveyor General’s Office (SGO) in the Ministry of Lands and resettlements, National Planning Commission (NPC) and surveys of their own as figure 11 above shows. A clear result to substantiate to lack of commitments in spatial data management is the result of Figure 12 below. The result indicates that about 33% of the organisations don’t know whether their spatial data are of quality standard, whilst 19% indicated to know that their data do not comply with the spatial data standard. Of all the above result, 48% indicated that their organisational data are accurate indeed. This result shows reasonable commitments in spatial data management in Namibia. The results are considered reasonable because 48% out of 100% is as good as 50%, also considering the fact that the category “Don’t know” can be neutral result.
Figure 12: Spatial data quality standard
On the implementation of data quality mechanisms for spatial dataset, 22% of the organisations in figure 13 indicated that they are in the processes of implementing the data quality control method. Another 22% indicated not to have knowledge about any of these activities. This also indicates the importance of spatial data management is understood by many organisations.
Figure 13: Quality control methods
Figure 14 below indicates that about fourteen of the organisations use specialist, whilst seven use external specialists. Five of the organisations have no quality control mechanism in place.
Figure 14: Spatial data quality standard
Spatial data is vital to any organisation, therefore, proper coordination of spatial data management is of importance in order to have good quality results and make meaningful quality decisions.
4.2.4 People and Training
People and Training are important aspects of a successful GIS. This is because without knowledgeable people, GIS will fail to exist today. The questionnaire results revealed that some organisations have a GIS system but no fulltime GIS specialist, administrator or staff member with fulltime doing GIS tasks. Figure 15 below shows that only 65% of the organisations have fulltime GIS specialist, while 35% have no GIS specialist or fulltime GIS staff member.
