The study investigates how Geographic Information System (GIS) spatial analysis and modelling improves demographic data analysis in the planning process (where demographic information and spatial analysis are strongly interrelated); i.e. seek for new ways and how the available 2D, 2.5D and 3D GIS spatial analysis and modelling methods can be manipulated to produce a set of techniques that can be used to derive different demographic variables and quantities for planning analysis.
A principal motivation for the GIS Demographic research is that planning analysis uses a lot attribute data about humans; where planning analyses require an indication as to how the total population and how selected composition groups are and will be spatially distributed in the study area making demographic data the main source of data into the planning process; and that most of the GI science planning analysis research is about GIS development rather than about GIS use, without a strong theoretical link between the two, to advance the GI science and to be useful in planning practice has to take the perspective of GI use as a move to direct way of providing a link between the science and planning practice, is the research theme of systematic evaluation GIS demographic spatial analysis and modelling.
In planning any area the growth potentials must be expressed in terms of the population it is expected to sustain – the size of population, its composition, and characteristics, and its spatial distribution. Although population data is collected at the point level (individuals and household), it is always aggregated to existing spatial entities (i.e. administrative units) to allow tabulations according to various data attributes and demographic analysis to be carried out using statistical techniques. The human Geographical dimensions of the information in demographic analysis are being forgotten most of the time, geography is only used to collect data; as a result information is lost, or hidden or details are difficult to extract, in additional being unable to view and analysis spatially in a way that produce demographic variables and quantities that are in line with planning analysis inputs.
To easily understand and fully utilize all of this demographic information there is need to carry spatial analyses at disaggregate level and to be linked to their locations to help in equity development. These problems correspond directly to the two key strengths of GIS - manipulation and display of spatially referenced data (Tomlin, 1990; Langford & Unwin, 1994; Chou, 1997; Chrisman, 1997; and DeMers, 1997, 2000), this is further facilitated by GIS’s capability to test and manipulate variables faster and as it is less expensive to test models rather than reality, and can predict consequences of proposed activities through simulation, which helps to pick "best" alternative.
Thus employ the techniques of 2D GIS, 2.5D GIS (DEM and DTM), and new techniques in form of three-Dimensional Demographic Model (3D-DM) in demographic data analysis and modelling, visualisation and interpretation; where other applications such as mining, hydrology, and environmental modelling have crossed over the 2D boundary into 3D modelling. This shift of application within GIS environment is necessary and is in line with other works by Yeh (1999) where he integrates GIS in planning; research by Lee (1995) in his PhD thesis at University of Washington where he develops a Methodology for Generating Alternative Land Use Plans Using GIS Modelling Techniques; and also GIS field came from the fields of spatial statistics, database management, and cartography.
Geographical information system (GIS) has been variously defined in many ways and by many people including Aronoff (1989), Huxhold (1991), ESRI (1992, 1994, 1998), Burrough (1986), Clarke (1986), Healey, et al. (1998), DeMers (1997, 2000), etc. But in Chrisman (1997) we find one of the most general definitions which was developed by consensus among 30 specialists: Geographical information system - A system of hardware, software, data, people, organization and institutional arrangements for collecting, storing, analysing, and disseminating information about areas of the earth. (Ducter and Kjerne, 1989)
The term "spatial analysis" encompasses a wide range of techniques for analysing, computing, visualizing, simplifying, and theorizing about geographic data. Methods of spatial analysis can be as simple as taking measurements from a map or as sophisticated as complex geocomputational procedures based on numerical analysis. Spatial analysis is statistical description or explanation of either Locational or attribute information or both (Goodchild, 1987). From Fischer, et al (1996) and Chou (1997) the spatial analysis include techniques such as spatial querying, point-in-polygon operation, buffering, overlaying, intersection, dissolving, proximity analysis, etc
Modelling, in GIS, it is used generally to refer to any operation involving the representation and manipulation of spatial data, particularly in composition of new features and coverages through the process of overlay (Burrough, 1986 and Tomlin 1990). It also has another meaning in the mainstream of system sciences, modelling involves simulation based on processes, which give rise to system structures (Batty, et al. 1994). In this research both definitions of modelling are employed.
Demography is the study of human populations with an emphasis on statistical analysis i.e. statistical characteristics (Plane, et al, 1994). Data describing a human population is referred to as demographic data. Involving primary the measurement of the size, growth, density, distribution, and diminution of the numbers of people, the proportions living, being born or dying within some area or region and the related functions of fertility, mortality and marriage (Cox, 1970). Demographics is often used in singular, meaning the application of demographic information and methods in business, planning, and public administration; and it is also seen in plural, referring to the demographic information itself (Merrick and Tordella, 1988). The major difference between demographics and the field of demography generally is that the later is concerned more with producing new knowledge and understanding of human behaviour, where as the former is concerned more with the use of existing knowledge and techniques to identify and solve problems (Weeks, 1994 pp. 477).
The main contention in this research is Demographic spatial analysis and modelling in two-dimensional (2D), two and half dimensional (2.5D), and 3D GIS, i.e. which aspects of demographic characterisation can be accomplished by 2D, 2.5D, 3D GIS, or a combination of them to produce demographic variables and quantities which are in line with planning analysis input using procedures that are common and easier to planners. Past research confirms that the GI-based tools developed by vendors and/or academics are for various reasons under-utilised (Harris 1989; Harris and Batty 1993; Holmberg 1994; lee, 1995 Klosterman 1997). Among reasons for under-utilisation of GIS in planning is incompatibility of the mostly generic GI products with the tasks and functions performed by urban and regional planners as it is one thing to have digital geographic information (Murray, 1999), but a far more challenging issue is how this information can be analysis/modelling and understood (what does the demographic data indicate or suggest and what are the implications) in planning environments.
Demographic data for planning analysis has been traditional analysed by statistical techniques where various models have been developed like Population Analysis Spreadsheets (PAS) for Excel, population change (plane, et al, 1994), spread model (Klosterman, et al 1994) has been used. Most of these models although they can account for change in demography, they lack the spatial aspect (it is not possible to geographically view and analysis the patterns) (Klosterman, et al, 1993); tends to ignore the demographic spatial dimension and if covered only at aggregate level; But there are approaches from different fields trying or which have taken advantage of GIS’s spatial analysis capability in order to incorporate the spatial (geographical) aspect, this has proved useful for understanding physical and environmental processes, the socio-economic dynamics are still hard to model and/or simulate, in terms of population analysis, the use of GIS in demographic data is not fully utilised, although it is rapidly expanding, as David Martin reports, the 1991 census of population was the first in United kingdom (UK) to be conducted in what might be called the ‘GIS era’; the 2001 census geography is designed by and for GIS (David Martin, 1997), and many others areas of integration like in Tiger (US census), PopMap - An Information and Decision Support System for Population Activities (UN web site).
In terms of 2D GIS various approaches have been proposed which include integration of spatial analysis methods in GIS that has lead to a new exploratory analysis (Goodchild, 1987; Haining, 1990; Fotheringham and Rogerson, 1993; Openshaw 1994b, c; and Openshaw et al. 1996) this has followed Fotheringham, et al (1994); Fisher, et al (1996); Carver (1997); Chou (1997); and DeMers (1997, 2000) that GIS is an incomplete set of spatial analytical tools, in many cases we are obliged test or combine GIS tools with statistical analysis and others in order to accomplish spatial analysis. Lot of research going on the linkage between spatial statistical analysis and geographic information systems, in Fotheringham, et al. (1994) the linkage has been basically suggested in three different ways. The first strategy is GIS and statistical packages like SAS and SPSS can be maintained as two separate packages and simply exchange data between the two systems, where write information from a GIS into a file and read this into statistical package to carry out analysis, then read back by GIS. In Carver (1997) we see that to export spatial data from the GIS to standard statistical systems is not an adequate solution, because the nature of spatial data requires specific spatial analytical functions. But Anselin et al. (1993) have combined SpaceStat, a program for the analysis of spatial data, with the Arc/Info using this approach. The second strategy is GIS functions can be embedded within spatial analysis or modelling. Although in caver (1997) it is noted that embedding GIS functions into a spatial statistical package seems to be an overwhelming exercise and not really realistic, examples which have taken place include XLisp-Stat Package which extend the geographical data handling and mapping facilities of a package designed for statistical programming (Tierney, 1991 and Openshaw et al. 1996). The third strategy, is Spatial analysis can be fully integrated within the GIS software. That a full integration of spatial analysis tools into a GIS seems most promising (Hansen, 1996), and that using this strategy we can utilize the interactivity between maps, charts and spatial statistics to get a good feeling of patterns and relationships within the data; examples include Arc/S-Plus (Arc/Info is linked to S-Plus), Spacestat integration with ArcView GIS by Anselin, Openshaw’s Geographical Analysis Machine (GAM) (Openshaw et al. 1987). Specialized GIS packages directed specifically at spatial analysis have emerged (Bailey and Gatrell (1995), Fisher et al. (1996), Haining (1990), Anselin and Getis (1993); and Anselin (1996, 1999), a good example is IDRISI for windows (IDRISI for windows, 1998). But all these are not directed towards micro demographic analysis from the planner’s point of view i.e. the inherent inability of the existing methods to provide useful results in planning analysis and the difficulty the planner often experiences in understanding what the results mean in relation to planning analysis is the issue here, not the integration. The principle need is to develop a style from the existing techniques and documentation of spatial analysis for GIS demographic analysis the planner (as a user of GIS) can use, not to force a planner to the methods that were created by experts for experts (Openshaw et al. 1996). Also knowing that GIS is not only 2D this has to be extended to 2.5D and 3D GIS.
The idea that population can most appropriately be mapped and modelled as a surface (2.5D) is not new, Schmid and MacCannell (1955) discussed the construction of contour-based maps of population density, while Nordbeck and Rystedt (1970) demonstrated that population density can be viewed as a continuously varying reference interval surface. Tobler (1979) presented a method for pycnophylactic (volume-preserving) interpolation of values from irregular zones into surface form, and Goodchild et al. (1993) review a number of approaches to areal interpolation, noting that the process can be viewed as involving the estimation of an underlying population surface; other developments are by Martin and Bracken (1991), with the latest developments being population geocoding, analysis, and modelling using grid by Martin (1999). All handle population surfaces by considering a value for a point as being representative for total for an area (or ratio based on total). Another shorting coming is that demographic variables are represented by two entities and it is these which differentiate them, for example gender, it either male or female; marital status is either single or married; etc. when these entities are modelled in GIS with the latest development in GIS surface analysis and modelling, we are able only to show their spatial locations and extents but not their spatial quantities. But for a planner is always looking how much and how demographic characteristics vary as move from one location to the next, that does not provide total solution for s/his needs. This is further made more complex by the fact that demographic quantities are required at different level of aggregation, thus the problem ranges from representing micro demographic data to aggregated data; also another factor, which comes in play, is the combination of the surface and the quantities (volumetric) analysis and modelling, which leads to the need for 3D GIS demographic analysis.
The central theme (thesis) is how demographic spatial analysis for planning analysis can be achieved in GIS by looking at two-dimensional, two and half dimensional, and three-dimensional spatial analysis and modelling to produce variables and quantities which are in line with planning analysis both at aggregated and disaggregated level.
The study is to investigate how GIS in terms of 2D, 2.5D, and 3D spatial analysis and modelling improves the demographic data analysis in the planning process; aim being to come out with documentation of set of techniques so that their inclusion in GIS may be facilitated. The following a priori (testable) objectives are formulated:
· To asses the current demographic data analysis and GIS (2D, 2.5D or 3D GIS) and carry out demographic spatial analysis and modelling.
· To model demographic characteristics into a three dimensional demographic model (3D-DM) to derive useful information for demographic characterisation and demographic quantities.
· To document how micro demographic data can be spatially analysed in GIS 2D, 2.5D and 3D) to produce variables and quantities which are in line with planning analysis
· Outline GIS demographic spatial analysis and modelling procedures in planning to come up with appropriate terminologies as well as enabling the use of them.
In meeting these objectives, a number of continuing themes become apparent; these are outlined in section 1.3 (Research approach i.e. Proposed Methodology).
The approach starts by a review of literature of problem at hand, then the main body which has been divided into three components a) overview, b) approach, and c) application; end with a conclusion giving the discussion and future work. The details are given under scope of research and have been divided into chapters (see thesis layout and figure 1.3).
Review of population (demographic) data in planning is the beginning point, which is followed by demographic statistical spatial analysis (DSSA) i.e. statistical spatial analysis methods for demographic analysis; methods of GIS data analysis and modelling; then a look at GIS in planning analysis; this leads to first task of this research i.e. GIS Demographic spatial analysis which involve looking and comparing DSSA and GISSA (Geographical information system spatial analysis i.e. GIS spatial analysis methods in use), what demographic analysis using GIS requires; how GISSA can be manipulated with an eye on results which are always expected from DSSA, to get GIS demographic spatial analysis (GISDSA) which can help to simplify and enhance demographic data analysis; at this stage the concentration is in the convectional 2D GIS and all is done following the Model of carrying out GIS Demographic Spatial Analysis (figure 1.1).
Figure 1.1 Model of carrying out GIS Demographic Spatial Analysis
Then proceed to Demographic analysis and modelling in 2.5D (surface analysis and characterisation). Look at the shorting coming of 2D GIS demographic analysis; then introduce the new demographic surface terms, their representation, and the derivation of parameters from the demographic surface and their interpretation.
From 2.5D GIS Demographic analysis and modelling, introduce 3D GIS Demographic spatial analysis and modelling. This is accomplished by employing the techniques from terrain analysis and modelling (DEM and DTM) in form of three-Dimensional Demographic Model (3D-DM) for demographic data representation, interpretation, visualisation and analysis. But before embarking on a detailed description of the nature of 3D GIS modelling its scope is defined by addressing a number of underlying questions. What should a characterisation of demographics in terms of surface attempt to achieve? How should demographic surface be modelled? Modelling the Third Dimension encompasses the following general tasks (see figure 1.2):
Figure 1.2: Three Dimensional Demographic Modelling Tasks
· 3D-DM generation: reading demographic from the database, formation of relations among the diverse observations (model construction);
· 3D-DM manipulation: modification and refinement of 3D-DMs, derivation of intermediate models;
· 3D-DM interpretation: 3D-DM analysis, information extraction from 3D-DMs;
· 3D-DM visualisation: graphical rendering of 3D-DMs and derived information; and
· 3D-DM application: development of appropriate application models for planning purposes. 3D-DM application in planning forms the context for 3D-Demographic modelling as each particular utilisation has its specific functional requirements relative to the other demographic modelling tasks.
To accomplish the objectives using the outlined methodology, use two types of data set to test techniques: One at very micro level collected from the Heritage area, in Georgetown, Penang state, Malaysia; The main information requirements are 1) the cadastral GIS of the study area, 2) buildings: floor space, number of floors, ownership, building location, and area etc 3) People: employment, age, sex, ethnic grouping, number of children, family members, etc and 4) Land use: shopping points, housing, recreation, etc. The demographic data collected from the study area; the cadastral data for the area already exist in GIS format obtained from Assoc. Prof. Dr. Lee Lik Meng, the building and land use data in GIS format from Penang state planning office (Jabatan Perancangan Bandar dan Desa) Penang, Malaysia. The other is population census data collected by Malaysia population and housing census office in 1991 census, which they publish at aggregated level of Mukim (parish).
The following will be used: ArcView to provide a graphical user interface (GUI) for direct interaction to view and edit geo-feature objects; ArcView Avenue (Customisation and Application Development for ArcView) programming environment, ArcView Spatial Analyst extension, ArcView 3D extension, Microsoft Access (Relational database management system (RDBMS)), and SPSS for statistical analysis. The link between these software packages is done using Microsoft Open Database connectivity and other import and export functions within these packages i.e. use SPSS Data Driver 32 (SPSS Data source 32) and ArcView SQL connection to use SPSS Data files in ArcView, SPSS Data source 32 and Microsoft Access to read SPSS files into Microsoft Access, using Microsoft Access Database and ArcView SQL connection to Database, to analysis data in SPSS use SPSS database capture using dbase files to read ArcView database files and Microsoft Access Database to read from the database.
The references being used include Books and periodicals, lectures, seminars, and discussions, computer software packages, and the Internet sites about GIS, planning, demography and population . All the work (thesis, references, links, and other research outcomes) are being hosted on School of Housing, Building, and planning (HBP) web site under thesis section http://www.hbp.usm.my/thesis/heritageGIS
This thesis consists of six chapters including the introduction, which highlights the research background, motivation, Problem statement and objectives of the research, methodology and the scope of the research.
Chapter 2 provides a global overview of demographic data in planning analysis; the concerns and methods of Demographic statistical spatial analysis (DSSA), GIS demographic data analysis and modelling which includes methods of geographical information system spatial analysis (GISSA), GIS in planning. It highlights the relative suitability of certain methods to particular applications and contrasts their differences, strengths and weaknesses; then GIS demographic analysis. It ends by introducing multi dimensional GIS for Demographic modelling where it outlines 2D, 2.5D, and 3D GIS to give an insight.
Chapter 3, starts with 2D GIS demographic analysis, then move on to examining the advances and work done in terms of demographic surface analysis and modelling, then derivation and interpretation of surface parameters; then introduce the aspect of 3D GISs; It discusses the need and criteria for a demographic surface representation, analysis, modelling using 3D GIS, which leads to chapter four dealing with 3D demographic analysis and modelling.
In Chapter 4, a complete modelling approach is described in details. Starting from demographic 3D modelling problems, definition and documentation of demographic terms to be used, analysed and modelled; then details 3D GIS (data structures, georeferencing, etc), the modelling of the third dimension taking field demographic data as the input, representation of Demographics as 3D spatial objects, describes the use of spatial tessellation using Voronoi diagram and triangular irregular networks (TIN) to construct and represent demographic characteristics where the issues of interpolation and extrapolation come into play; conversion of data points into triangular irregular networks, a new method to improve the modelling quality of these TINs is described i.e. Algorithms for generating triangular irregular networks in three dimensions (3D TIN) will be developed to come out with the 3D TIN to be used in the 3D-DM, demographic quantitative modelling.
Chapter 5 gives visualisation of demographic data - here the concentration is using the existing GIS, Scientific, and Geographical visualisation techniques. Then, uncertainty in GIS analysis where fuzziness is considered in the methods of Demographic analysis and modelling not in errors in data obtaining/observation and storage; Followed by the evolving the GIS DM, where the concern is about integration the developed models with other datasets. This chapter concludes by looking at a possible structure of utilising these techniques in planning analysis.
Finally, Chapter 6 discuses the merits and limitations of this approach and a comparison with closely related current work by summarising the research, documents and recapitulates the main issues (results) and the study contributions obtained throughout the research and highlights areas of future work. Figure 1.3 shows the main partitions of this thesis and a visual overview of its contents.
Figure 1.3: visual overview of the main portions of the thesis
 For references quoted in the thesis without year of publication are only from the internet due to the fact that some authors don’t indicate years and these pages are always changing, but that does not mean all internet references don’t have years, some do have.