Building Spatial Data Infrastructures for Spatial Planning in African Cities: the Lagos Experience Samuel Dekolo1 and Leke Oduwaye2 Department of Urban and Regional Planning, Lagos State Polytechnic, Ikorodu, Lagos Nigeria Email:
[email protected] 2 Department of Urban and Regional Planning, University of Lagos, Akoka Lagos Email:
[email protected] 1
Abstract: Lagos is the fastest growing Megacity in Sub-Saharan Africa, with its population estimated to double in the first quarter of this century; it is expected to be the third largest urban agglomerations in the world. This growth is not without challenges, as the city is grappling with myriads of urban management problems. City planners lack the most important ingredient of land use management, which is Information. In spite of huge investment on spatial data infrastructures at the national and state levels of government, most land use planners at both state and local government level agencies are ignorant of existing geospatial technology portals and unlock the full potentials of information and communication technologies. A statewide survey of the spatial data infrastructures of the city’s urban and land use management ministry and agencies proves its pathetic state, thereby creating information gap void between urban development and intelligent management. The result is has led to a sporadic growth of slums and unplanned settlements which now accounts for over 60% of the city. To avoid an impasse, it is necessary to review the level of geospatial technologies used at the local level and recommend formidable means of integration in the decision making process. This paper examines the level of geospatial technologies and Spatial Data Infrastructure use in spatial planning agencies and barriers to implementation in the 20 local governments of Lagos State and suggests the way forward. Keywords: Information and Communication Technology, planning agency, Spatial Data Infrastructures, geospatial technologies, spatial planning
Introduction Effective management of cities involves mobilizing diverse resources to work in cooperative manner in the field of planning, programming, budgeting for the development, operation and maintenance of every part of the city in order to achieve the developmental objectives. The most important issues in city management are obtaining knowledge of the location of resources, land availability, infrastructure requirement and developing integrated planning strategies to enable the channelling of resources to deal with such requirements (Shalaby, et al., 1996). Geospatial technologies provide useful tools that will un-earth the socioeconomic, environmental and sustainability variables,
which can aid intelligent decision-making. These technologies empower the planner to detect, analyse and predict possible urban changes through modelling techniques. The availability of communication networks can also enhance data sharing, transfer and reduce planning response time to urban change. In spite of the immense potentials of automated technologies in city planning and management, the benefits remain unlocked in most African countries, due to poor adaptation and implementation. Lagos State Government recently implemented a web-based enterprise GIS that will serve as the bedrock for e-government, known as LAGIS. The objectives of the project include ensuring effective use and application of geospatial information in providing sustainable planning and development of the ever-growing Megacity. It will also provide effective inventory and monitoring of the environment, while also providing a navigation system for smart transportation. The project emanated from recommendations made by the presidential committee on ‘Redevelopment of the Lagos Megacity Region’ and previous attempts made to develop a statewide GIS (FGN, 2006; Dekolo & Oduwaye, 2011). The implementation of the project was structured into six broad technical modules. The acquisition of digital aerial imageries and establishment of geodetic control; establishment of Geoid and active Global Positioning System and reference station with Continuous Operating Reference Station; digital mapping, Digital Terrain Model (DTM) and orthophoto maps; enterprise GIS, bathymetry chart; and provision of information and communication technology infrastructure, as well as education and training. While there is a cry of ‘Uhuru’ on the outcome of the project, there is need for empirical evidence from planning agencies to ascertain the level of spatial data infrastructures implementation for spatial planning and city management. The research focused on the Ministry of Physical Planning and Urban Development (MPPUD), which is the agency responsible for spatial planning in Lagos State; it performs its role through its four subagencies, which has city and state-wide jurisdictions. These include Physical Planning Department (PPD), Lagos State Urban Renewal Authority (LASURA), Lagos State Planning Permit Agency (LASPPA) and the Lagos State Building Control Agencies (LASBCA). This research aims at appraising the level of accessibility and availability of spatial data infrastructures (SDIs) in planning agencies responsible for managing the Lagos Megacity. A survey approach with the distribution of structured questionnaires to state and local planning agencies in the 20 local government areas was adopted to appraise the level of SDI implementation and awareness, the use of geospatial technologies for planning and managing the Lagos Megacity. The survey recorded 100% response was recorded in the state-wide agencies, while 45% and 25% response was recorded from the local planning permit (LASPPA) and building control agency (LASBCA) respectively. We may not rule out apathy and official secrecy from the low response of local planning agencies. However, the survey result reveals gaps in SDI awareness and geospatial knowledge; there is difficulty in access of spatial data due to lack of infrastructure and lapses in policy implementation. The paper concludes with a recipe for a sustainable Geospatial Data Infrastructure for managing the Lagos megacity.
Literature Review Spatial information has become indispensable for planning and management of cities. The management and planning of urban space requires spatially accurate and timely information on land use and changing pattern. Adequate monitoring provides the planners and decision-makers with required information about the current state of development and the nature of changes that have occurred (Gadou & Taha, 2004). There cannot be an overemphasis of the import of Geospatial technologies in providing vital tools for urban management at various levels of planning and the planners’ capacity for decision-making. However, in most African countries, geospatial information are developed, used and maintained by government and private agencies independently; efforts have been agency-focused, uncoordinated and duplicated. The concept of Spatial Data Infrastructure (SDI) has emerged as a panacea to facilitate cooperative production, use and sharing of geospatial data. SDI usually comprises of data itself, standards, policies, technologies, and procedures for different agencies and organization to cooperatively produce, use and share geographical information. Thematic issues on Spatial Data Infrastructures emanating from the global north have identified the need for information systems as decision support for urban planning (Batty, 1991; Batty & Densham, 1996), nature and framework for national, regional, and local SDIs (Rajabifard, et al., 1999; Rajabifard, et al., 2000; Feeney, et al., 2001). SDI research in African countries have focussed on several themes as fundamental datasets (United Nations Economic Commission for Africa, 2007), hierarchical structures of SDI, framework for implementation (Shalaby, et al., 1996), interrelationship between levels of governments (Ogundele & Somefun, 2008; Smit, et al., 2009) and review of SDI implementation status (Makanga & Smit, 2010). Though few researches delve into specific urban applications like housing delivery (Agunbiade, 2012) and inter-agency cooperation for land management (Agunbiade & Rajabifard, 2013), there is however a gap in understanding the dynamics of SDI development at the corporate level in African cities. If we have to build SDI blocks from local to global scales, there is need for scrutiny of constituents that makes up the lowest level of SDI or its foundation, which is the corporate SDI. There are several public and private organizations or agencies responsible for creating and using spatial data for city planning and management, however, this research focuses on the ‘planning agency’ as a corporate entity. 2.1
The Planning Agency and the Need for Spatial Information
Spatial information has become indispensable for urban planning and management of cities. The management of urban space requires spatially accurate and timely information on land use and changing pattern. Adequate monitoring provides the planners and decision-makers with required information about the current state of development and the nature of changes that have occurred (Gadou & Taha, 2004). Geospatial technologies provide essential tools for urban management at various spatial scales and enhance an informed decision-making process.
Information has been described as the basic resource in all decision making (Dale & Mclaughlin, 1988). This corroborated early researcher like Faludi (1973) drew an analogy between the planning agencies and the human mind, in which the planning agency is seen as an intelligent person who engages in purposive thinking and contemplates implementing a change in his environment. He further relied on Weiner’s Cybernetic Model, in which the planning agency tends to guide and control development in the city by depending on information-flow, which after being processed will lead to exercising control. According to Faludi (1973), “very small amounts of energy in form of information flows, guide the expenditure of substantially larger amount of process which have physical basis and can be measured, analyzed and manipulated. In short, information guide and control the activity of the planning agency”.
Figure 1: The Model of the Planning Agencies showing information flow in the Local Planning Authorities (Faludi, 1973)
The model described by Faludi in Figure 1 depicts how information flows within the planning agency. The planning agency gathers information about the environment through its survey unit (receptor) and this is stored in the memory of the agency. The development plan section prepares statutory development plans, which draws information from surveys and images the planning agency has for the environment. Development plans entails programmes developed from clear-cut goals, which guide the planning committee in decision-making and the development control section (effector) in administering planning permit for erection of physical structures that has direct effect upon the environment. The cybernetics process in planning comes to fore, where a little investment of funds and energy to gather information and producing a programme goes a long way in controlling large amount of resources, physical development and people, in a nutshell, controlling the environment (Faludi, 1973). Automation of activities in the planning agency is possible through the application of information and communication technologies.
2.1.1
Automation of the Planning Agency and its Theoretical Underpinnings
Proliferation and adaptation of information and communication technologies in the field of urban and regional planning can be attributed to its importance in strategic planning. Automating the planning process requires a combination of human and technical resources within a set of organizational procedures that produces information in support of some corporate obligations (Dale & Mclaughlin, 1988; Pettit & Pullar, 2000). This fundamental expedition for automating the urban and regional planning process and the use of computer-based models in planning practice and education is not a new one. In fact, the concept of spatial data infrastructures, which zeros down on data sharing can be traced back to the 1960s. Early scholars saw the need for a real-time information system in the management of our urban areas; they envisioned systems that receives data, process them and return results sufficiently quickly to affect the functioning of the environment at that time (Martin, 1965). Furthermore, the emergence of urban planning theories like systems theory opened a vista for the automated planning process. Shalaby et al (1996) identified the systems theory as having a dominant role in the advancement of geospatial technology applications in planning. The theory is concerned with developing a systematic, theoretical framework for describing general relationships of the empirical world. The system approach, which was used in the management sciences in the 1960s, dominated planning in the 1990s. This approach utilizes a set of methods, techniques, and intellectual tools collectively known as systems analysis for complex problem solving (Lapatra, 1973; Batty, 1991). The systems approach is generally applied to large problems, in which the common characteristic of problem is addressed rather than independently. However, progress in planning theory has witnessed a paradigm shift to new information-driven planning frameworks as functional and communicative rationality. According to Budic-Nedovic (2000), functional rationality is based on a modernist and positivist ideal, which puts information gathering and scientific analysis at the core of planning, this is because, it is assumed that there is a direct relationship between the information available to the planner and the quality of decisions based on this information. Communicative (substantive or procedural) rationality, which is a postmodernist concept, focuses on an open and inclusive planning process, public participation, dialog, consensus building, and conflict resolution; this also requires information sharing by all stakeholders. Larsen (2003) in conceptualizing e-planning as an extension of information and communication technology applications in urban and regional planning, identified two dimensions of its definition, which is based on horizontal dimension that deals with the product (the plan) and vertical dimension that is the process (planning). Using Danish local municipalities, he evaluated the levels of adaptation and implementation using four quadrants. Both dimensions interplay on each quadrant as follows:
Analog Plan-Analogue Planning (AA): Plans are made through planning process not supported by digital aid, where analyses are carried out manually (in the head), communication is done by traditional mail, meetings and telephone and where plan is distributed traditionally as printed document.
Digital Plan-Analogue Planning (DA): Plans are made through a planning process not supported by digital aid, where analyses are carried out ‘in the head’, communication is done by traditional mail, meetings and telephone and where the plan is distributed via the internet in a digital format (Scanned document. e.g. Adobe PDF). Analogue plan-Digital Planning (AD): Plans are made through a planning supported by digital aid, where analyses are carried out in a computer like GIS and traffic models and where debate and dialogue about the plans are supported by the internet e.g. chartrooms and e-mails, and where plan is distributed traditionally as a printed document. Digital Plan-Digital Planning (DD): Plans are made through a planning process supported by digital aid, where analyses are carried out in computer like GIS and traffic models and where debate and dialogue about the plans are supported by the internet e.g. chartrooms and e-mails, and where plan is distributed via the internet e.g. as webpage.
Fig. 2: Vertical and Horizontal Dimensions of e-planning (Modified from Larsen, 2003)
Larsen (2003)submitted that adaptation of ICT and related technologies in planning is driven by the increasing demand of efficiency, cost reduction and effectiveness. A second but minor driving force is the focus on increased quality and service level in the work carried out by the municipal or local government administrations. The application may also be classified into two dimensions:
D1: Data accessibility and data processing. The amount of available data and information is growing rapidly and the tools to manipulate and analyze these data and this information is getting more accessible and easier to use. D2: Communication independently of time and place. The possibility to communicate across the world via the internet at a low cost and high speed.
Using the SITAR model developed by Hudson (1979), Larsen further explains how ICT affects planning when seen from a planning theory perspective. The SITAR model brings together five planning traditions: Synoptic Planning, Incremental Planning, Transactive Planning, Advocacy Planning and Radical (or Recalcitrant) Planning. This is summarized in the table below.
Table 1: ICT Applications in Urban Planning and Planning Theory Planning Theory
Paradigmatic Core
Rationality Type
ICT – D1
ICT – D2
Synoptic planning
Search for the best Possible combination of means for given ends
Instrumental rationality
D1 improves and supports the analytic dimension
D2 is not utilized specifically
Incrementalism
Search for satisfactory alternative, given an unclear and partly collapsed means-end scheme
Bounded instrumental rationality
D1 improves and supports the analytic dimension
D2 improves communication between planning actors, but communication is just a mean and not an end in itself
Transactive planning
Organize dialogue to promote democracy and personal growth and search for a solution agreed upon in undistorted communication
Communicative rationality
Utilized as a mean to improve the information level for communication but not considered as a primary objective
New canals for communication and the internet’s support for decentralized communication fits perfectly into Transactive planning
Advocacy
Counteract structural communicative distortions to promote equal opportunities and build support for a reasonable effective and fair alternative
Bounded communicative rationality
Utilized to improve the information level for communication but not considered as a primary objective.
New canals for communication holds both a threat and a potential for improvement because of the digital divide
Radical or recalcitrant planning
E.g.: Political rationality preserves and improves decision structures to prevent indecisiveness and internal conflict
Other types of rationality
Utilized as a mean to improve the information level for communication
Supports communication on a grass-root level
(Source: Larsen, 2003)
2.1.2
Evolution of ICT and Spatial Technologies in Urban and Regional Planning
Information and Communications Technologies have been applied in urban and regional planning since the 1950s with various simulation modelling efforts in demographic and transportation applications (Batty & Densham, 1996). The Urban Transport Planning (UTP) process is an example of the earliest computer-based simulations and standardized approach to modeling the urban transport system. More so, with combination of mathematical concepts of land use model in the 1950s and 1960s, this led to the development models as Gravity model known a Lowry model (Llyod-Jones, 1996). These models which were also analytic were simple structured based on mathematical and linear programming using computer simulations. By the late 1960s, urban data management systems were being widely implemented by public agencies for a variety of routine and less routine management and strategic planning functions (Batty & Densham, 1996).
Computer mapping system was first developed in the 1970s known as SYMAP (symbol maps), but did not enter the planning mainstream because it was still at the experimental stage and expensive, but in the 1980’s miniaturization made it possible for dissemination and affordability of the computer technology (Batty, 1991). From that time till now there has been a steady growth in both software and hardware development with special applications to urban planning and management have the capabilities of spatial analysis, query, modeling, mapping integration and other planning applications. The last decade has experienced new technological developments like distributed processing, mobile mapping, internet mapping, network communications, and other software and hardware deployment transcending the spatial database and mapping system, which Geographic Information System (GIS) was known for in the 1990s. Spatial Data Infrastructure is one of these technological developments are being adapted to decision-making and management functions of planning process. 2.2
Towards the Spatial Data Infrastructure Concept
Spatial Data Infrastructure (SDI) is an initiative intended to create an environment that enables a wide variety of users to access and retrieve complete and consistent data sets in easy and secure way. As a tool, it provides an environment in which all stakeholders can cooperate with each other and utilize technology in a cost effective way to better achieve the objectives at the appropriate political and administrative level, i.e. local, national, regional or global level (Rajabifard, et al., 2000). It is defined as the technology, policies, standards, and institutional arrangements necessary to acquire, process, store, distribute and improve the utilization of geospatial data from different sources and for a wide range of potential users. SDI comprises of fundamental geospatial datasets, documentation of these data in form of metadata, clearinghouses, human resources and partnerships, policies and standards to promote capture, use, reuse and exchange of geoinformation (GSDI, 2002; EIS-AFRICA, 2002). Some salient element of SDI that has most often been overlooked are the institutional and economic elements like organizational arrangements, political support, social considerations, educational and legal aspect, funding, cost recovery and making a business case to justify the project (Woldai, 2003). Lacks of consideration of these factors have often hindered SDI implementation. 2.2.1
Interoperability and Hierarchies of SDI in the Context of Spatial Planning
Spatial data infrastructure transcends a geographic information systems infrastructure, because it is the important interoperability and spatial integration element of information driven society (Rajabifard, et al., 1999). Previous research reveals that, there is a nexus between inefficiencies in land management and lack of spatial data integration across agencies at various levels of government in Nigeria. Even though most state and local agencies cover common jurisdiction, there is lack of integration in process and policies, thereby making housing delivery and urban development cumbersome (Agunbiade & Rajabifard, 2013). However, there is the need to also understand the level of SDI preparedness of these agencies in terms of physical infrastructures and core spatial data, with a focus on the spatial planning.
Spatial planning is a new paradigm in planning based on interoperability across space, agencies (private and public), and policies. It is an approach in planning that coordinate policies and investment across agencies to achieve a common objective for activity places, which may span communities to supra-national spaces. It aims at coordinating spatial dimension of sectoral policies (environment, economic, social, transport, etc) and integrate conflicting or competing areas through territorially based strategy (Cullingworth & Nadin, 2006; United Nations, 2008). While spatial planning integrates policies across various spatial levels, SDIs provides the information linkage from local to global scales.
Figure 3: Relationship between Levels of Spatial Planning and SDI (Rajabifard, et al., 1999)
SDIs could be viewed from two hierarchical perspectives, the umbrella, and the building block as illustrated in Figure 4A and 4B. The umbrella view depicts a higher level SDI encompassing all the components of a lower level SDI. For example, the Global Spatial Data Infrastructure (GSDI) is the SDI at the global level, which encompasses all Regional SDIs, National SDIs, State SDIs, and Local SDIs. This includes the policies, technologies, standards and human resources necessary for the effective collection, management, access, delivery and utilization of geospatial data in a global community, which trickles down to the local level (Rajabifard, et al., 2000). The building block view, on the other hand, takes a bottom-up approach, in which SDIs at a lower level (e.g. local government level) serve as the supporting building blocks to provide spatial data needed by SDIs at a higher level in the hierarchy such as the state, national or regional levels. This facilitates data sharing, transfer and partnerships, while reducing the overall cost of data collection. Based on these two views, the SDI hierarchy creates an environment in which decision-makers working at any level can draw on data from other levels, depending on the themes, scales, currency and coverage of the data needed (Rajabifard, et al., 1999).
Figure 4 A) Umbrella View of SDI B) Building Block View of SDI (Rajabifard, et al., 2000)
Taking a critical look at the building block view of SDI, we discover that the corporate SDI is the foundation and lowest level of SDI on which other blocks rests. Chan and Williamson (1999) had earlier argued that a good understanding of the nature and dynamics of development a corporate SDI would benefit other SDI implementation from local to global levels. Using the Department of Natural Resources and Environment in the State Government of Australia, their investigation zeroed on the pattern of GIS development and its characteristics in order to gain insight for managing SDI development at higher levels. In their submission, performance or production in the corporate setting is dependent on two things; the infrastructure GIS and the business process GIS, with the former supporting the later (Chan & Williamson, 1999). This accentuates the need for an assessment of corporate SDIs, which the planning agency is the subject of this research.
Research Methodology The research adopted a survey approach, in which sets of questionnaire were administered in agencies and deparments of the Lagos State Ministry of Physical Planning and Urban Development across the Local Government Areas of Lagos State. The following are the agencies: Regional and Master Planning Department, Physical Planning Department, Development Matters, Urban Development, Lagos State Urban Renewal Agency, Lagos State Planning Information Centre, Urban Furniture Regulatory Unit, Lagos State Physical Planning Permit Authority and Lagos State Building Control Agency. The New Towns Development Authority (NTDA) was also included in the survey. The investigation covered availability of ICT infrastructure, awareness of SDI concept, access to LAGIS website, availability of core SDI data, data sharing and coordination, perceived barriers and benefits of ICT in urban planning. The survey recorded 100% response from state level agencies, while response only 30% response was recorded at the local level agencies, however, respondent agencies were spread across 12 of the 20 Local Government Areas, which also represent 58% of the total land mass of the state. 54.5% of the respondents are in the professional cadres, while respondents in the technical and administrative cadres were 36.4% and 9.1% respectively. The survey results were analysed using the Statistical Package for Social Sciences to derive the findings below.
Findings and Discussion The results of the investigation were summarized under the key components of SDI, which includes availability and access to information and communication technologies infrastructures and networks, spatial technologies capacities, knowledge of SDI concept, availability of core SDI data, perceived barriers and benefits of ICT in urban planning. 4.1
Findings on Information and Communications Technologies Infrastructures
The survey generally shows a very low level of access to communications infrastructures except for the use of GSM phone for official communications which every respondent have access to and communications on the internet, which 64% of the respondent have access to. However, only 27% of the respondents use the internet for official communications. The lack of communications networks have a major significance on how planners communicate vertically and horizontally. While it is expected that local area networks should be available in all the planning agencies, only 36.4 % had access to local area network, while 22.7% use a wide area network and PABX. Virtual Private Network (VPN) services, which are available from most GSM networks, are rarely used with only 9% using such services. Table 1.Access to Communications Networks Response %
Mean
Communication Networks
Rank
Yes
No
Access
GSM
100
0
1.00
1
Internet
63.6
36.4
0.64
2
Local Area Networks
36.4
63.6
0.36
3
PABX
22.7
77.3
0.23
4
Wide Area Networks
22.7
77.3
0.23
4
Radio Communication
13.6
86.4
0.14
6
VPN/CUG
9.0
91.0
0.09
7
(Source: Field Survey, 2014)
On hardware in use, the survey shows that 82% of the respondents have access to personal computers in planning offices, and 95% have access to laptops for official use. However, the use of such computer hardware is limited to routine planning tasks of report writing, correspondence and other non-spatial related uses. The non-use of geospatial software in most local and state planning agencies corroborates this. Moreover, other peripherals used for GIS like plotters and digitizers are vividly lacking in all the local planning agencies. 14% representing few state level agencies like the New Towns Development Authority (NTDA). Lagos State Planning Permit Authority (LSPPPA) headquarters and Lagos State Planning Information Centre (LASPIC), which is the GIS hub for the Ministry of Physical Planning and Urban Development; each of these agencies have a GIS studio/lab. As seen in table 3, 91% of the planning agencies use general applications software packages like Microsoft Office, while a core GIS Software like ArcGIS has only 18.2%
usage and web-based GIS application packages has only 9.1% usage. The only common spatial planning software in use, which is principally for design, i.e., AutoCAD has only 50% in all the agencies. Table 2. Computer Hardware in Use Response % Hardware in Use
Mean
Rank
Yes
No
Use
Laptops
95.0
5.0
0.95
1
Printers
91.0
9.0
0.91
2
Desktop PCs
82.0
18.0
0.82
3
Scanners
77.0
23.0
0.77
4
Palmtops/Tablets
23.0
77.0
0.23
4
Plotters
14.0
86.0
0.14
6
Digitizers
14.0
86.0
0.14
7
(Source: Field Survey, 2014)
In table 3., Mapwindows and QGIS both have 22.7% and 4.5% usage respectively; though this is quite low, it is an indication that some agencies are already embracing Open Source GIS software to overcome the challenge of cost. Less than 10% make use of web-based GIS applications, this cannot be divorced from lack of awareness of these platforms. The LAGIS enterprise website, which is even a state owned platform, had 27% of the respondents attempting to access it, while only 23% of the respondent succeeded in doing so. Findings show that there is a shortfall in information infrastructure of the spatial planning agencies, which is an essential for SDI development. Table 3. Software in Use Response % Software in Use
Mean
Rank
Yes
No
usage
General Applications (Microsoft Office)
91.0
9.0
0.9
1
AutoCAD
50.0
50.0
0.5
2
Mapwindows
22.7
77.3
0.23
3
ArcGIS
18.2
81.8
0.18
4
WebGIS Applications
9.1
90.9
0.09
4
Intergraph
9.1
90.9
0.09
6
QGIS
4.5
95.5
0.05
7
ILWIS
0
100
0
8
(Source: Field work, 2014)
4.2
The Use, Usability and Usefulness of the LAGIS Enterprise GIS
The LAGIS Enterprise GIS project was intended to provide to access spatial data to various categories of users in the public to private agencies. The site provides users with several layers of data ranging from orthophoto and cadastral maps as seen in figure 5a and 5b to administrative boundaries, land use, road network, water bodies, and other layers bothering various acquisition lands in the state. Planners are able to identify parcels, buildings, and related data stored in the LAGIS database.
Figure 5: A) LAGIS Enterprise GIS Orthophoto Layer B) Cadastral Map Layer
The LAGIS website provides users with the capabilities of exporting and viewing its footprints as layers on web applications as Google Earth and ArcGIS Explorer, which makes it free for users in planning agencies to access, view and print (see figures 6 below). However, this can only be possible with a high-speed internet connection, which is not presently available in most planning agencies. The LAGIS server could also be viewed though a link to the ArcGIS Mapserver by pasting the web address on your web browser. This has been made possible through the ArcGIS Applications Programming Interface (API) for JavaScript (see figures 7a-c).
Figure 6: Land Use and Acquisition Layers imported and viewed in Google Earth
Figure 7: LAGIS Data Viewed at Different Scale on Web Browser (source:http://gisapps.lagosstate.gov.ng/ArcGIS/rest/services/LAGIS_MINNA/MapServer?f=jsapi)
The LAGIS server is embedded with potentials for data sharing among spatial data users and could enhance sustainable urban management. However, success of SDI development may not be divorced from the discovery, access, and usability of spatial data (Budhathoki & Nedovic-Budic, 2007). This study reveals this missing link; even though 55% of the respondents are aware of the LAGIS project, only 23% successfully accessed and used the LAGIS Enterprise GIS for planning purposes. Most of the respondent could not rate the project owing to the fact that they could not access it; while only 13.6% of the respondents found the project useful or very useful.
4.3
Availability of Fundamental Geospatial Datasets for Spatial Planning
Fundamental geospatial datasets, which are produced within the existing institutional framework by collecting from primary sources or those derived from other data by integration or adding value, must be consistent within its geographic coverage. It is necessary to take a cue from Faludi’s planning agency by assessing the availability of fundamental datasets required by the planning agencies to obtain the following result: Table 4. Availability of Fundament Geospatial Datasets for Spatial Planning Response % Fundamental Geospatial Datasets
Approved Private Layouts Aerial Photographs Digital Satellite Imageries Development Permit Register Government Residential Schemes Land Use Zoning Topographic Data Transportation Model City Plans Industrial Schemes Administrative Maps Land Cover/Vegetation Renewal/Regeneration Maps Hydrological Data Climate Cadastral/Land Tenure Health Data Demographic/Population Wetland Maps Soil Maps Forestry Economic Data Geodetic Data Bathymetry Data
Valid % Valid
None 0
Paper 1
Digital 2
Paper /Digital 4
Mean
Rank
Missing
20
2
35.0
45.0
15.0
5.0
0.90
1
21
1
57.1
14.3
14.3
14.3
0.86
2
21
1
61.9
4.8
19.0
14.0
0.86
2
21
1
42.9
42.9
4.8
9.5
0.81
4
20
2
45.0
35.0
15.0
5.0
0.80
5
21
1
52.5
28.6
9.5
9.5
0.76
6
21
1
52.4
33.3
9.5
4.8
0.67
7
21
1
57.1
23.8
14.3
4.8
0.67
7
21
1
61.9
19.0
9.5
9.5
0.67
7
20
2
70.0
15.0
15.0
0.0
0.45
10
21
1
76.2
14.2
4.8
4.8
0.38
11
21
1
76.2
9.5
14.3
0.0
0.38
11
20
2
75.0
20.0
5.0
0.0
0.30
13
21
1
85.7
4.8
9.5
0.0
0.24
14
21
1
85.7
4.8
9.5
0.0
0.24
14
21
1
85.7
9.5
4.8
0.0
0.19
16
21
1
85.7
9.5
4.8
0.0
0.19
16
21
1
85.7
9.5
4.8
0.0
0.19
16
21
1
90.4
4.8
4.8
0.0
0.14
19
21
1
90.4
4.8
4.8
0.0
0.14
19
21
1
90.4
4.8
4.8
0.0
0.14
19
21
1
90.4
4.8
4.8
0.0
0.14
19
22
0
95.5
0.0
4.5
0.0
0.09
23
21
1
100.0
0.0
0.0
0.0
0.00
24
(Source: Field work, 2014)
The result in table 4 above shows a great gap in fundamental dataset available for spatial planning in the city of Lagos. 73% of all the agencies do not have the fundamental data available, while 15% have data types in paper format. However, digital format which is necessary for sharing and SDI is only limited to 12% of the agencies. This calls for necessary policy action on making datasets in the LAGIS website and other platforms available to these agencies. 4.4
Interagency Relationships and Data Sharing and Coordination
Data sharing has been the crux of early SDI discussions and literature, however, new theoretical directions have seen the need of the 3Cs (collaboration-cooperationcoordination) and intergovernmental Relations (Budhathoki & Nedovic-Budic, 2007; Masser, et al., 2008). The research reveals is no framework for data sharing among various agencies in the state, since the Draft Lagos Geoinformation Policy is not yet signed into Law. The draft policy did not address the technical details of the data sharing. One of the challenge experienced so for in implementing the policy is the pricing model for spatial data to be downloaded from the LAGIS website. In other words, the Lagos State Government does not know how much to sell data to its own agencies. Since the MPPUD relies on some other custodian ministries like the Ministry of Lands for spatial data, it cannot access reliable data for its planning functions. Moreover, there is no coordination to facilitate easy data sharing even among its own agencies; most custodians of spatial information place departmental ownership interest above societal benefits.
Figure 8: Agencies of the Lagos State Ministry of Physical Planning and Urban Development (source: Adapted from 2012 Ministerial Press Briefing (MPPUD, 2012) )
4.5
Perceived Benefits to Spatial Data Infrastructures for Planning
The human side of SDI is as important as its technical side. Techno-centric approaches to GIS implementation have shown to fail especially in developing countries. Early involvement of users in the development of SDI is a factor for success (Budhathoki & Nedovic-Budic, 2007). In creating a platform for involvement, we examined the users’ perception on the benefits of ICT and geospatial data infrastructures in urban and regional planning agencies. This will give an insight to the motivation for using ICT in spatial planning agencies. 75 % of the respondents agreed that ICT and geospatial tool are beneficial to spatial planning, but 21% are unsure, while only 6% disagree. Table 5.Percieved Benefits of ICT and Geospatial Data Infrastructures in Spatial Planning Agencies Response % RANK
Parameter Unsure
Strongly Disagree 1
Disagree
Agree
2
Somewhat Agree 3
4
Strongly Agree 5
Improved Communication and Speed Reduce Cost of Operation/ Services Saves Time
18.2
0.0
0.0
0.0
27.3
54.5
1
18.3
0.0
4.5
4.5
22.7
50.0
2
22.7
0.0
0.0
4.5
22.7
50.0
2
Better Design Production
18.2
0.0
0.0
4.5
27.3
50.0
2
Increases Productivity
18.2
0.0
0.0
9.1
27.3
45.5
5
Data Accuracy and Integrity Effective Data Integration and Analysis It reduce travel cost
18.2
0.0
4.5
36.6
18.2
45.5
5
22.7
0.0
0.0
0.0
36.4
40.9
7
18.2
0.0
4.5
4.5
36.4
36.4
8
Effective Data Access and Distribution Work away from office
22.7
0.0
4.5
4.5
31.8
36.4
8
22.7
0.0
22.7
13.6
13.6
27.3
10
Revenue Generation
27.3
4.5
22.7
9.1
18.2
18.2
11
Mean
20.7
0.4
5.76
8.3
25.6
41.3
(Source: Field work, 2014)
The major motivation for the use ICT in the planning agency as perceived by respondents is the improved communication and speed, while operation cost reduction, time, and better design products also forms a major factor. Revenue generation is the least perceived motivation for geospatial data infrastructures. 4.6
Barriers to Spatial Data Infrastructures: A Factor Analysis Approach
Understanding the perceived barriers from the users’ perspective is helpful in a bottomup approach of SDI implementation and it will give directions to policy. We adopted the use of factor analysis to understand the most significant group of independent variables and reduce the numbers from 19 to 8. This approach has been has been used previously to understand the SDI capacities of Local Government in Australia (McDougall, et al., 2009). Even though most authors will recommend larger sample sizes for factor analysis, we examined the suitability of our data by the Kaiser-Meyer-Olkin Measure of Sample Adequacy (KMO) and Bartlett’s Test of Sphericity, in which the KMO result was 0.587 (approx. 0.6) and the Bartlett’s significance value is 0 (see Table 7). This
initial result confirms the suitable of our data for factor analysis (the KMO must be 0.6 minimum and the Bartlett’s significance value must be 0.5 or less). In addition, another initial test passed is the values in the correlation coefficient of most values are above 0.30. In determining the number of components to extract, we used a principal component analysis method to extract components with initial Eigenvalues above 1.0; five components explained 87.2% of the total variance (see table 6). However, the scree plot (see figure 9) shows an elbow bend at the second component. These first two component were selected because it explains more percentage of the variance than others altogether (about 67.3% of the total variance). In a subsequent factor analysis using two factors extraction and an oblimin rotation, the result shows a more correlated pattern (see Correlation Matrix in Appendix, Pattern matrix in Table 10 and pattern plot in Figure 10). Table 6: Total Variance Explained Based on Using Eigenvalues >1.0 Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadingsa
Total
% of
Cumulative %
Total
Variance
% of
Cumulative
Variance
%
Total
1
10.861
57.162
57.162
10.861
57.162
57.162
7.207
2
1.933
10.172
67.334
1.933
10.172
67.334
7.348
3
1.476
7.770
75.104
1.476
7.770
75.104
7.582
4
1.260
6.632
81.736
1.260
6.632
81.736
1.327
5
1.041
5.481
87.217
1.041
5.481
87.217
3.143
6
.758
3.987
91.204
7
.388
2.043
93.246
8
.308
1.619
94.865
9
.273
1.437
96.303
10
.245
1.289
97.592
11
.154
.808
98.400
12
.129
.676
99.076
13
.082
.430
99.506
14
.038
.202
99.708
15
.032
.166
99.874
16
.011
.060
99.935
17
.008
.044
99.979
18
.003
.015
99.994
19
.001
.006
100.000
Extraction Method: Principal Component Analysis. a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance.
(Source: Field work, 2014) Table 7: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity
Approx. Chi-Square
.587 504.202
df
171
Sig.
.000 (Source: Field work, 2014)
Figure 9: Scree Plot Showing Eigenvalues and Components (source: Field Work, 2014) Table 8: Total Variance Explained Based on Two (2) Number of Factors Initial Eigenvalues
Component
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings a
Total 1
10.861
2
% of Variance
Cumulative %
Total
57.162
57.162
10.861
1.933
10.172
67.334
1.933
3
1.476
7.770
75.104
4
1.260
6.632
81.736
5
1.041
5.481
87.217
6
.758
3.987
91.204
7
.388
2.043
93.246
8
.308
1.619
94.865
9
.273
1.437
96.303
10
.245
1.289
97.592
11
.154
.808
98.400
12
.129
.676
99.076
13
.082
.430
99.506
14
.038
.202
99.708
15
.032
.166
99.874
16
.011
.060
99.935
17
.008
.044
99.979
18
.003
.015
99.994
19
.001
.006
100.000
% of Variance
Cumulative %
57.162
57.162
9.613
10.172
67.334
8.659
Extraction Method: Principal Component Analysis. a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance. Table 9: Component Correlation Matrix Component 1 2 1
1.000
.619
2
.619
1.000
Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization. (Source: Field work, 2014)
Total
Table 10: Pattern Matrixa Showing Variables Selected in Component 1 & 2 Component 1 Lack of Long Term Vision
2
1.020
No Industry Standards and Policy
.914
Poor Data Quality
.872
High Cost of Specialized Software
.805
Lack of Executive Support
.773
Political Constraints
.758
Privacy Constraints
.704
ICT not adequate in professional Education and Schools Legal Constraints
.676 .615
Not Acceptable to Planning Professionals
.600
Poor Budget/Funding
.549
.448
Lack of Core ICT Personnel
.941
Manual Planning is Better
.877
Lack of Awareness
.858
Lack of ICT Training for Staff
.845
Very Expensive to use
.777
Cost of Staff Training is High
.713
Difficult to integrate into Planning Professional .344 .572 Practice ICT is used more for Social than Professional .317 .547 Use (eg. Facebook) Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.a a. Rotation converged in 6 iterations. (source: Field Work, 2014)
Figure 10: Component Plot in Rotated Space Showing Correlation between Components 1 & 2 (source: Field Work, 2014)
Eight variables with highest loading were selected from components 1 and 3 in the pattern matrix table. The implication is that more attention must be paid to issues, which bothers on long-term vision and goals of the SDI project, industry standards and geoinformation policy, employing key ICT and GIS personnel in the agencies, GIS education, and training, creating more awareness among planners on the importance and benefit of a wholesome geospatial data infrastructure. Another major challenge is the high cost of software, which open sources and government subsidy may play a major role. Previous researches have suggested ways to address some of the issues identified above (Agbaje & Akinyede, 2005; Budhathoki & Nedovic-Budic, 2007; McDougall, et al., 2009; Dekolo & Oduwaye, 2011). However, there is need for speedy passage of the Lagos State Geoinformation Policy into Law; Clearinghouses and node agencies should be established to serve spatial planning agencies; the academia should also be involved through SDI oriented research and GIS education should be strengthened in Lagos planning schools.
Conclusion and Further Research Access to spatial data for planning is a major challenge in managing cities in Africa, however, SDIs provides a platform for data sharing and use in a most cost effective way. This research has been able to appraise the level of spatial data infrastructures in the spatial planning agencies in the Lagos Megacity by assessing the state and local level agencies in Lagos State. It has closed the gap of lack of empirical research in evaluating SDI capacities of urban and regional planning agencies responsible for managing the Lagos Megacity. The article has also examined theoretical underpinnings of geospatial technologies applications in urban and regional planning, with an emphasis on spatial data infrastructures. The study reveals that urban and regional planners lack of access to existing SDI platforms, which is of LAGIS Enterprise GIS due to poor ICT infrastructure. While improved and speed of communications is the highest motivation for the use of geospatial technologies, revenue generation, which is central to the state government is not approved by its planning agencies. The research also identified the greatest barrier to SDI is lack of clear-cut and long-term vision on SDI. Further research is suggested in the area of developing a model clearinghouse at the Lagos Urban Observatory. This will serve as a model node for corporate SDI in Lagos State. The Lagos Urban Observatory should collaborate with the Lagos State Government and international agencies in metamorphosing into a node for urban spatial data. This will be s springboard for replicating urban observatories in other cities in Nigeria.
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Appendix
ICT not adequate in professional Education and Schools
No Industry Standards and Policy
Lack of Executive Support
Difficult to integrate into Planning Professional Practice
Cost of Staff Training is High
High Cost of Specialized Software
Manual Planning is Better
Not Acceptable to Planning Professionals
Poor Budget/Funding
Legal Constraints
Privacy Constraints
Lack of Long Term Vision
Poor Data Quality
Political Constraints
Lack of Long Term Vision Poor Data Quality Political Constraints
ICT is used more for Social than Professional Use (eg. Facebook)
Privacy Constraints
Lack of Core ICT Personnel
Lack of ICT Trained Staff Very Expensive to use Lack of Core ICT Personnel ICT is used more for Social than Professional Use ICT not adequate in professional Education and Schools No Industry Standards and Policy Lack of Executive Support Difficult to integrate into Planning Practice Cost of Staff Training is High High Cost of Specialized Software Manual Planning is Better Not Acceptable to Planning Professionals Poor Budget/Funding Legal Constraints
Very Expensive to use
Lack of Awareness
Lack of ICT Trained Staff
Correlation
Lack of Awareness
Correlation Matrix Using 2 Components
1.000
.805
.527
.752
.755
.405
.502
.471
.697
.410
.460
.734
.305
.583
.358
.354
.498
.482
.315
.805
1.000
.739
.905
.760
.382
.489
.636
.568
.556
.411
.552
.412
.713
.515
.542
.495
.514
.540
.527
.739
1.000
.797
.515
.441
.317
.535
.602
.796
.595
.428
.322
.715
.495
.492
.367
.419
.591
.752
.905
.797
1.000
.745
.373
.426
.632
.585
.649
.406
.578
.406
.642
.508
.475
.367
.455
.514
.755
.760
.515
.745
1.000
.503
.682
.565
.606
.375
.492
.516
.333
.538
.396
.386
.588
.630
.604
.405
.382
.441
.373
.503
1.000
.659
.394
.522
.396
.638
.249
.359
.438
.240
.281
.672
.577
.486
.502
.489
.317
.426
.682
.659
1.000
.750
.480
.191
.593
.261
.480
.576
.423
.413
.789
.690
.640
.471
.636
.535
.632
.565
.394
.750
1.000
.554
.372
.576
.288
.403
.764
.847
.803
.690
.620
.675
.697
.568
.602
.585
.606
.522
.480
.554
1.000
.555
.723
.743
.314
.759
.633
.600
.658
.657
.371
.410
.556
.796
.649
.375
.396
.191
.372
.555
1.000
.618
.595
.469
.654
.510
.492
.380
.531
.357
.460
.411
.595
.406
.492
.638
.593
.576
.723
.618
1.000
.355
.601
.695
.578
.584
.780
.788
.514
.734
.552
.428
.578
.516
.249
.261
.288
.743
.595
.355
1.000
.310
.613
.391
.385
.441
.506
.101
.305
.412
.322
.406
.333
.359
.480
.403
.314
.469
.601
.310
1.000
.489
.239
.336
.615
.659
.326
.583
.713
.715
.642
.538
.438
.576
.764
.759
.654
.695
.613
.489
1.000
.775
.791
.759
.696
.574
.358
.515
.495
.508
.396
.240
.423
.847
.633
.510
.578
.391
.239
.775
1.000
.935
.604
.607
.502
.354
.542
.492
.475
.386
.281
.413
.803
.600
.492
.584
.385
.336
.791
.935
1.000
.654
.607
.498
.495
.367
.367
.588
.672
.789
.690
.658
.380
.780
.441
.615
.759
.604
.706
.891
.635
.482
.514
.419
.455
.630
.577
.690
.620
.657
.531
.788
.506
.659
.696
.607
.654
.706 1.00 0 .891
1.000
.577
.315
.540
.591
.514
.604
.486
.640
.675
.371
.357
.514
.101
.326
.574
.502
.607
.635
.577
1.000
