Rohan Wickramasuriya

Using geospatial business intelligence to support regional infrastructure governance

In many developed countries including Australia, infrastructure services at local and state levels are being provided by an increasing number of disjointed public and private agencies. There is an urgent need for an integrated view on the provision and use of these services for better governance and productivity. Developing an integrated view is challenging due to the dispersion of relevant data sets and the underlying complexity of increasingly interconnected infrastructure networks. Using a case study in New South Wales (Australia), we demonstrate how tools and processes in Geospatial Business Intelligence (Geo-BI) can be harnessed using a collective design approach to develop an integrated solution; the SMART Infrastructure Dashboard (SID). While providing a much needed planning and policy support tool for the local governance of infrastructure services, SID pushes the boundary of Geo-BI beyond its traditional use.

A multi-scale geospatial study of wetlands distribution and agricultural zones, and the case of India.

This paper highlights the global and the regional scale representation of wetlands ecosystems using geospatial tools and multiple data sets. At global scale, the Ramsar database is investigated for representation of the wetlands sites of international importance against the “global agricultural zones” derived from the thematic aggregation of Global Irrigated Area Map databases. The analysis of “Ramsar sites” under cultivation reflects the present trend in wetlands use for agriculture. The scenario is also compared with the historical pattern derived from Vavilovs food zones of 1926. Observed is an aggregate increase in cropped wetlands area from 25% (1926) to 43% (2006). The second component develops a regional partnership with Salim Ali Centre for Ornithology and Natural History in India. The partnership reviews the thematic national database of inland wetlands and “priority wetlands habitats” (PWH) in comparison with the bio-geographic and agro-ecological factors (regions/sub-regions) and by means of geographical information system (GIS) tools. We elaborate the strength of spatial tools to better understand the relationship between wetlands distribution and agricultural zones, both historically and at the present time. The disseminated message states, though from a technical perspective, the understanding of scale and resolution in combining information from diverse sources is essential; the effective implementation of spatial analysis requires a true cross-disciplinary approach. Complementing that, relevant policy support and appropriate institutional arrangements are fundamental to advance the management work required for unification of wetlands conservation with the existing challenges of food and livelihood security.

An approach to plan and evaluate the location of radiotherapy services and its application in the New South Wales, Australia

This paper proposes an integrated modelling approach for location planning of radiotherapy treatment services based on cancer incidence and road network-based accessibility. Previous research efforts have established travel distance/time barriers as a key factor affecting access to cancer treatment services, as well as epidemiological studies have shown that cancer incidence rates vary with population demography. Our study is built on the evidence that the travel distances to treatment centres and demographic profiles of the accessible regions greatly influence the uptake of cancer radiotherapy (RT) services. An integrated service planning approach that combines spatially-explicit cancer incidence projections, and the placement of new RT services based on road network based accessibility measures have never been attempted. This research presents a novel approach for the location planning of RT services, and demonstrates its viability by modelling cancer incidence rates for different age-sex groups in New South Wales, Australia based on observed cancer incidence trends; and estimations of the road network-based access to current NSW treatment centres. Using three indices (General Efficiency, Service Availability and Equity), we show how the best location for a new RT centre may be chosen when there are multiple competing locations.

A method to dynamically subdivide parcels in land use change models

Spatial simulation models have become a popular tool in studying land use/land cover (LULC) change. An important, yet largely overlooked process in such models is the land subdivision, which is known to govern LULC change and landscape restructuring to a large extent. To fill this gap, we propose an efficient and straightforward method to simulate dynamic land subdivision in LULC change models. Key features in the proposed method are implementing a hierarchical landscape where adjacent cells of the same LULC type form patches, patches form properties, and properties form the landscape and incorporating real subdivision layouts. Furthermore, we use a queue-based modified flood-fill algorithm to dynamically reset LULC patches following a subdivision. The proposed subdivision method is demonstrated in action using a prototype agent-based LULC model developed for an amenity landscape in Australia. Results show that it is computationally feasible to run the subdivision method even as spatial resolution is increased, thus providing a proven means for spatial simulation models to dynamically split parcel land.

Insight to Ecosystem Based Approach (EBA) at Landscape Level Using a Geospatial Medium

Ecosystem based approach (EBA) for resource management is a concerted, environmentally tuned and an integrated framework that holistically addresses the ecological character of the natural resource, its societal benefit spectrum and its environmental functions. In this paper, the EBA concept is closely linked with the emerging concept of multiple use systems (MUS) while taking account of environmental, economic, and social factors that govern the ecosystems services and benefits. We elucidate a multi-scalar approach and multiple case studies to understand EBA particularly in context of a wetlandscape. At the global scale, Ramsar sites of international importance are geospatially analyzed with reference to their agro-ecology and biodiversity. At regional scale, the agrarian use of inland wetlands in India was re-evaluated taking account of database from a recent inland wetland inventory. At the local scale, drawing on the landscape characterization and the ecological economics for fresh water Lake Kolleru in India and the Muthurajawela Marsh-Negombo Lagoon coastal marsh in Sri Lanka, we illustrate some of the practical challenges in balancing wetland conservation, development needs and the overall well-being of local people. We also discuss how variability in the scale, geophysical characteristics of the site and the data availability confines the ability to simplify a single complete approach to address issues in complex ecosystem such as wetlands. All levels of the study are supported by a variety of earth observation data and the geographical information system (GIS) tools. The site level analysis also draws on socio-economic assessment tools.

A Semi-Empirical Determination of Perceived Liveability

Liveability is a concept closely related to the notion of well-being and refers to the environmental conditions that contribute to the quality of life, alongside individual features. Subjective and objective measurements of liveability are both of considerable practical and theoretical importance. A survey is conducted in this paper by which individuals tend to shape their preferences according to six factors describing various aspects of living conditions: (1) home, (2) neighbourhood, (3) transport, (4) entertainment, (5) services and (6) work. The survey data helps us to work out some indicators representing the perceived liveability in the targeted areas. A linear mixed model is used to explore possible relationships between objective factors and perceived liveability. A model-based estimate of liveability index can be then calculated for each non-sampled individual based on his/her socio-demographic characteristics and area of living.

A conceptual method for modeling residential utility consumption using complex fuzzy sets

In many countries including Australia, residential utility consumption, as a primary measurement of infrastructure service at local and state levels, is affected by many influential factors such as different varieties of utilities, local community profiles and regional climate conditions. Due to the fact that the information of a regional residential utility consumptions and their influential factors are often held separately by different public and private agencies, there is an urgent need among the communities, the utility providers, and the utility administration organizations for an integrated view on local residential utility consumption and usage for better utility service and governance. Developing such an integrated view is challenging due to the dispersion of relevant data sets at various temporal and spatial scales and the underlying complexity of increasingly interacting factors. By using complex fuzzy sets to describe uncertainty and periodicity features at various temporal and spatial scales, this paper presents a conceptual method for modeling residential utility consumption in the development of a geographic-business-intelligence-based infrastructure information platform. Through the presented method, cross-organization residential utility consumption pattern can be extracted through a knowledge-based pattern mining technique. This work can be used for providing an integrated view on the entire infrastructure service to support relevant decision making.

Hybrid method for building extraction in vegetation-rich urban areas from very high-resolution satellite imagery

Abstract. A continuous update of building information is necessary in today’s urban planning. Digital images acquired by remote sensing platforms at appropriate spatial and temporal resolutions provide an excellent data source to achieve this. In particular, high-resolution satellite images are often used to retrieve objects such as rooftops using feature extraction. However, high-resolution images acquired over built-up areas are associated with noises such as shadows that reduce the accuracy of feature extraction. Feature extraction heavily relies on the reflectance purity of objects, which is difficult to perfect in complex urban landscapes. An attempt was made to increase the reflectance purity of building rooftops affected by shadows. In addition to the multispectral (MS) image, derivatives thereof namely, normalized difference vegetation index and principle component (PC) images were incorporated in generating the probability image. This hybrid probability image generation ensured that the effect of shadows on rooftop extraction, particularly on light-colored roofs, is largely eliminated. The PC image was also used for image segmentation, which further increased the accuracy compared to segmentation performed on an MS image. Results show that the presented method can achieve higher rooftop extraction accuracy (70.4%) in vegetation-rich urban areas compared to traditional methods.

Modelling mode choice of individual in linked trips with artificial neural networks and fuzzy representation

Traditional mode choice models consider travel modes of an individual in a consecutive trip to be independent. However, a persons choice of the travel mode of a trip is likely to be affected by the mode choice of the previous trips, particularly when it comes to car driving. Furthermore, traditional travel mode choice models involve discrete choice models, which are largely derived from expert knowledge, to build rules or heuristics. Their approach relies heavily on a predefined specific model structure (utility model) and constraining it to hold across an entire series of historical observations. These studies also assumed that the travel diaries of individuals in travel survey data is complete, which seldom occurs. Therefore, in this chapter, we propose a data-driven methodology with artificial neural networks (ANNs) and fuzzy sets (to better represent historical knowledge in an intuitive way) to model travel mode choices. The proposed methodology models and analyses travel mode choice of an individual trip and its influence on consecutive trips of individuals. The methodology is tested using the Household Travel Survey (HTS) data of Sydney metropolitan area and its performance is compared with the state-of-the-art approaches such as decision trees. Experimental results indicate that the proposed methodology with ANN and fuzzy sets can effectively improve the accuracy of travel mode choice prediction.

Population accessibility to radiotherapy services in New South Wales Region of Australia: a methodological contribution

This paper proposes an integrated modelling process to assess the population accessibility to radiotherapy treatment services in future based on future cancer incidence and road network-based accessibility. Previous research efforts assessed travel distance/time barriers affecting access to cancer treatment services, as well as epidemiological studies that showed that cancer incidence rates vary with population demography. It is established that travel distances to treatment centres and demographic profiles of the accessible regions greatly influence the demand for cancer radiotherapy (RT) services. However, an integrated service planning approach that combines spatially-explicit cancer incidence projections, and the RT services accessibility based on patient road network have never been attempted. This research work presents this novel methodology for the accessibility assessment of RT services and demonstrates its viability by modelling New South Wales (NSW) cancer incidence rates for different age-sex groups based on observed cancer incidence trends; estimating the road network-based access to current NSW treatment centres; and, projecting the demand for RT services in New South Wales, Australia from year 2011 to 2026.