Tung-Kai Shyy

Integrating attribute and space characteristics in choropleth display and spatial data mining

This paper develops an interactive approach for exploratory spatial data analysis. Measures of attribute similarity and spatial proximity are combined in a clustering model to support the identification of patterns in spatial information. Relationships between the developed clustering approach, spatial data mining and choropleth display are discussed. Analysis of property crime rates in Brisbane, Australia is presented. A surprising finding in this research is that there are substantial inconsistencies in standard choropleth display options found in two widely used commercial geographical information systems, both in terms of definition and performance. The comparative results demonstrate the usefulness and appeal of the developed approach in a geographical information system environment for exploratory spatial data analysis.

Fear of crime in Brisbane Individual, social and neighbourhood factors in perspective

Numerous theories apply to fear of crime and each are associated with different kinds of variables. Most studies use only one theory, though this study examines the relative importance of different kinds of variables across a number of theories. The study uses data from a survey of residents in Brisbane, Australia to examine the relative importance of individual attributes, neighbourhood disorder, social processes and neighbourhood structure in predicting fear of crime. Individual attributes and neighbourhood disorder were found to be important predictors of fear of crime, while social processes and neighbourhood structure were found to be far less important. The theoretical implications are that the vulnerability hypothesis and the incivilities thesis are most appropriate for investigating fear of crime, though social disorganization theory does provide conceptual support for the incivilities thesis. Although social processes are less important in predicting fear of crime than neighbourhood incivilities, they are still integrally related to fear of crime: they explain how incivilities arise, they buffer against fear of crime, and they are affected by fear of crime.

An On-line Planning Support System to Evaluate Urban and Regional Planning Scenarios

This chapter describes the development of a planning internet site incorporating a suite of spatial decision support system tools to undertake multi-scaled planning analysis. Current functionality allows users to perform dynamic analyses of regional demographic and employment trends toformulate thematic map and associated tabular data results. At the local urban level, users can review the results of a number of ‘what-if’ planning scenarios based on a multiple criteria evaluation approach. The site has been built at the regional levelfor the Wide Bay-Burnett region in Queensland, Australia, and within it, at the local urban level for the rapidly growing and expanding Hervey Bay local government area.

Developing a web‐based e‐research facility for socio‐spatial analysis to investigate relationships between voting patterns and local population characteristics

This paper describes the development of an e‐research facility for socio‐spatial analysis. It is illustrated with the example of a prototype Web‐based GIS and statistical application for the analysis, modelling and visualisation of the relationships between patterns of voting at the 2007 Australian federal election and the demographic and socio‐economic characteristics of local populations using 2006 census data. The facility incorporates a web‐based GIS which can generate maps displaying patterns of voting for political parties across polling booths with overlay data showing the population characteristics living within the surrounding polling booth catchments. Various classification approaches including equal interval, quantile, median‐based natural breaks, and location quotients can be used to generate different map displays. Statistical analysis functionality ‐ such as regression analysis, cluster analysis and discriminant analysis ‐ enables researchers to conduct on‐line statistical modelling and the visualisation of outputs. This prototype facility not only gives researchers and students on‐line access to socio‐spatial datasets through a metadata directory, but also enhances the capacity and capability of researchers and students to undertake spatially integrated social science research.

Web-based GIS for mapping voting patterns at the 2004 Australian federal election

This paper describes a Web-based geographical information system (GIS) for mapping voting patterns, at the 2004 Australian federal election, at the polling booth level. The locations of polling booths are geocoded and linked with national digital datasets, including the 2001 census. The Web-based GIS can generate maps displaying patterns of voting for political parties across polling booths with overlays of data showing the demographic and socio-economic characteristics of populations within the surrounding polling booth catchments. A classification functionality consisting of equal interval, quantile, median-based natural breaks and location quotient may be used in order to generate different map displays. The Web-based GIS has been developed as an information dissemination and analysis tool to not only benchmark voting outcomes but also to visualise relationships between voting patterns and local demographic and socio-economic data.

Enabling SDMX-based Retrieval and Spatio-statistical Analysis of National Census and Related Datasets

This paper describes a collaborative project between the Australian Urban Research Infrastructure Network (AURIN), the Australian Bureau of Statistics (ABS), the University of Queensland eResearch Lab and a number of social science research centres across Australia - that provides programmatic access to ABS Census data sets to enable its re-use within a range of research projects. The project successfully demonstrates machine-to-machine access to 2011 Census data through a federated data hub model that dynamically delivers statistical datasets to the research community. Using an SDMX web services approach, combined with advanced data analytics and visualization services available through the AURIN workbench, social scientists are able to manipulate and visualize national census data overlaid with other related data sets through a sophisticated mapping interface. Integrated statistical analysis services (R-based) also enable social scientists to quantify correlations between different demographic and socio-economic parameters. A number of socio-economic use-cases are presented that illustrate how the system enables researchers to understand and quantify changes in industry sectors, labor force needs, employment, population needs and disadvantage, over space and time. The paper also outlines problems and limitations revealed through the demonstrator projects, lessons learnt and areas that will require further effort to deliver optimum access to national census data sets and associated e-social science infrastructure for both the Australian and global social science community.

Statistical analysis and visualization services for Spatially Integrated Social Science datasets

The field of Spatially Integrated Social Science (SISS) recognizes that much data of interest to social scientists has an associated geographic location. SISS systems use geographic location as the basis for integrating heterogeneous social science data sets and for visualizing and analyzing the integrated results through mapping interfaces. However, sourcing data sets, aggregating data captured at different spatial scales, and implementing statistical analysis techniques over the data are highly complex and challenging steps, beyond the capabilities of many social scientists. The aim of the UQ SISS eResearch Facility (SISS-eRF) is to remove this burden from social scientists by providing a Web interface that allows researchers to quickly access relevant Australian socio-spatial datasets (e.g. census data, voting data), aggregate them spatially, conduct statistical modeling on the datasets and visualize spatial distribution patterns and statistical results. This paper describes the technical architecture and components of SISS-eRF and discusses the reasons that underpin the technological choices. It describes some case studies that demonstrate how SISS-eRF is being applied to prove hypotheses that relate particular voting patterns with socio-economic parameters (e.g., gender, age, housing, income, education, employment, religion/culture). Finally we outline our future plans for extending and deploying SISS-eRF across the Australian Social Science Community.

Subjective Quality of Life in Queensland: Comparing Metropolitan, Regional and Rural Areas

The chapter provides a comparison of the subjective assessment of quality of life (QOL) across different scales of urban communities in Queensland, Australia. Univariate and multivariate analyses of survey data relating to respondents’ assessments of four main attributes of urban environments are presented, namely, access to services and facilities, noise pollution, incivilities and social capital. Specific hypotheses are tested to determine if and how subjective QOUL in regional cities and towns differ from those in metropolitan and rural areas, and which main attributes of urban environments best distinguish regional cities and towns from metropolitan and rural areas.

Modelling endogenous regional employment performance in non-metropolitan Australia: what is the role of human capital, social capital and creative capital?

Over the years many studies have been conducted in Australia investigating regional spatial differentials in regional economic performance, including investigations of the inter-relationships between regional economic growth, population growth, structural shifts in employment distribution across industry sectors, industrial diversification, levels of income, and the location of regions in the national settlement system. It has been relatively common for variables relating to human capital to be included in such studies, but rarely has research incorporated a consideration of social capital and creative capital. There are difficulties in pursuing such research because there are no national data sets that explicitly provide operational measures of human capital, social capital and creative capital. As such, it is necessary to use data derived from the Census of Population and Housing to form proxy measures relating to those constructs.