Advances in Civic Co-management within the Geospatial Ecosystem Applied to Disaster Risk Management

The use of mobile devices for identifying risk and coordinating disaster response is well accepted and has been proven as a critical element in disaster risk management. As new tools, applications, and software are adopted by municipal governments and NGOs for the identification and management of urban risk, the need for greater integration of the various data they collect becomes acute. While the challenge of integrated data management is substantial, it is aided by the fact that many new tools have been developed to include an Application Programming Interface (API), which allows the machine-to-machine (i.e. automated) sharing of open data. While some proprietary platforms for the management of urban data are currently available, they are extremely costly and very limited in terms of data inputs; to date there are no open source geospatial software tools for the integrated management of various API sources to evaluate hazards for disaster response. A key to improving disaster risk management as an element of risk identification is the development of an integrated open source Decision-Support Risk Evaluation Matrix that enables: 1) automated integration of multiple geospatial and non-geosapatial API sources into a low cost, user-oriented dashboard; 2) backend database and software design for the Risk Evaluation Matrix that enables data sources to be parameterized and interrogated; 3) the development of an output API stream that allows additional secondary applications to optimize their evaluations and analyses through open access to critical risk information. To address these challenges this paper presents an open source Risk Evaluation Matrix, currently in development, which aims to provide situational oversight of flood hazards from multiple data-sources, including social media, in the city of Jakarta, Indonesia

SMART Infrastructure Dashboard: A Fusion between Business Intelligence and Geographic Information Systems

Abstract: Business Intelligence (BI) has popularly been adopted as a process that enables easy access, analysis and visualization of information through specialized set of tools for informed decision making. Two most noticeable characteristics of traditional BI is that it (a) is largely used in single-organization environments and (b) uses predominantly aspatial data. We believe that BI has applications beyond single-organization environments, but it very much requires integration of geospatial capabilities given the increasing availability of large volumes of spatial data and a growing interest to see things spatial. The SMART Infrastructure Dashboard (SID), our innovative solution that fuses BI and Geographic Information Systems (GIS), fills this significant gap. In this study, we demonstrate how SID can be used to perform spatio-temporal analysis and visualization of diverse sets of data to uncover complex interrelationships among utility usage, demographics and weather patterns at local and regional scale. Citation: Wickramasuriya, R., Ma, J., Somashekar, V., Perez, P. & Berryman, M. (2014). SMART Infrastructure Dashboard: A Fusion between Business Intelligence and Geographic Information Systems. In: Campbell P. and Perez P. (Eds), Proceedings of the International Symposium of Next Generation Infrastructure, 1-4 October 2013, SMART Infrastructure Facility, University of Wollongong, Australia

Mapping for the future: Business intelligence tool to map regional housing stock

The amount of data available and the lack of data integration represent an increasing challenge to effective planning for government agencies. Integration of data from multiple sources has the potential to enable a user to draw valuable insights, which can be used to enhance service targeting and delivery, and to improve program evaluation. In recognition of the need to improve data integration the University of Wollongong and the NSW Office of Environment and Heritage (OEH) partnered to create an integrated housing stock database for the Illawarra region. The database serves as the backbone for an online and interactive Housing Stock Mapping Dashboard (HSMD). It assembled multilevel granular information (including at the Statistical Area Level 1 (SA1) and Local Government Area (LGA) level) collected from multiple historical programs by multiple agencies. This centralised, integrated data repository can help agencies understand the existing housing stock, and improve access to information to support evidence-based policy. This paper presents a model of how data can be integrated from multiple agencies to provide an online collaboration platform. The platform, HSMD, was designed to demonstrate to government, industry, and the research community the opportunity of data integration and advanced analytics. Potential applications of the HSMD include characterisation of the existing housing stock according to a range of building attributes, for instance the presence of ceiling insulation or rainwater tanks. Comparison of these attributes with energy consumption data can indicate the influence of the attribute, or the impact of a specific intervention. This can help policy makers understand uptake and penetration of previous rebate schemes

Do we have wicked problems and solutions in strategic decision making?: A review of literature

In today\u27s complex world, most businesses and companies experience different confusing strategy issues. Most of these problems are not just persistent or severe but are labelled as wicked in some areas of practice such as urban planners. In this context, the term \u27wicked problems\u27 refers to those issues which companies and businesses cannot resolve definitively. This paper proposes that applying wicked solutions to wicked problems might be an effective strategy that requires an appropriate decision-making approach by senior leaders of business organisations. It also provides ways of measuring outcomes of some strategic decisions. The paper will use the existing body of knowledge and different conceptual models to investigate the research idea empirically in a sample of senior leaders and CEOs

Developing and testing new spatio-temporal modelling techniques to explore the dynamics of changing rural landscapes in Australia

Amenity-led counterurbanization is driving a multifunctional transition in rural lands of Australia. Fundamental to this transition is the subdivision of large farming properties as traditional farmers exit agriculture, acquisition of subdivided lots by affluent and mobile urban populations that seek a lifestyle change in rural areas, and diverse land management activities adopted by an increasingly heterogeneous mix of land owners. This transition poses new socio-economic and environmental challenges. A better understanding of these changes and their implications is essential to manage the transition with the least negative impact on socio-economic and environmental conditions. This is quite challenging given the complexity of processes and heterogeneity of actors involved in changing rural landscapes. Spatial simulation modelling techniques, particularly agent-based land use/land cover (LULC) modelling have been applied elsewhere to understand such complex dynamics. However, existing models cannot be directly applied to study Australian amenity landscapes mainly because of the inadequacy of structure, system elements and encompassed processes that limit the ability of such models to sufficiently describe amenity landscapes and their dynamics. Based on these premises, the broad aims of this thesis were to 1) develop, verify and validate new tools and methods to address critical gaps in existing spatial simulation modelling practices that limit how amenity migration researchers and land use modellers are able to frame their research questions, and 2) demonstrate the use of newly developed tools and methods for amenity migration research in particular and LULC modelling research in general. This thesis first developed a novel, fully-automated land subdivision tool that uses vector data and is capable of generating complete subdivision layouts with both lot and street arrangements for land parcels of any shape. When the new streets are generally parallel to each other and lots are of approximately the same size, the simulated subdivision layouts very closely resemble observed subdivision patterns in the south-eastern Australia study area. From this validation exercise, the opportunities to improve the subdivision tool for a next version were also identified. The thesis then presents an innovative method to simulate land subdivision in LULC change models using subdivision layouts generated either by the tool developed in this thesis or by alternative tools. This method is demonstrated with a prototype agent-based LULC change model developed for an amenity landscape (Windellama, New South Wales, Australia). Central to the success of the proposed method is implementing a hierarchical landscape where adjacent cells of the same LULC type form patches, patches form properties and properties form the landscape. The second key element of the method is incorporating real subdivision layouts. The necessary addition of new streets during a subdivision changes both the LULC type and the LULC patches to which some cells belong. An innovative queue-based modified flood-fill algorithm is used to reset LULC patches following a subdivision. Results show that this algorithm is computationally efficient even as spatial resolution is increased. Achieving the right balance between computational efficiency and detail of representation is crucial in LULC change models, and the results show that this balance is reached at 50m resolution for the modelled area. Finally in the thesis, a comprehensive agent-based LULC change model is developed around the previously described prototype model. This model includes six agent types (graziers, hobby farmers, green lifestylers, non-farming retirees, absentees and real estate agents), simulates realistic land subdivision, and incorporates a detailed endogenous land market. The agent types show clear differences in the socioeconomic attributes and land management strategies they adopt. Verification shows that this model is robust, while validation gives confidence that the representation of land use dynamics for the main LULC classes is sufficiently realistic. Long term experimental simulations with the model reveal that internal buying and nonmonetary conditions imposed on land transactions have a significant effect on LULC and demographic change trajectories in amenity landscapes. Experimental results also suggest that the size of lots decided at the subdivision stage can alter the ownership composition in amenity landscapes in the long run, and thereby affect the course of LULC change. In summary, the tools, methods and models developed in this study lay the groundwork for amenity migration researchers to gain new insights into changing rural landscapes by enabling them to frame research questions that would have been impractical to answer if not for these key innovations. Importantly, the technical innovations introduced in the thesis are pertinent to entire land use modelling discipline, thus demonstrating the broad applicability of the findings of this thesis

A Star Schema for Utility Network Analysis and Visualisation in a Geo-Business Intelligence Environment

Abstract: Utility network analysis is an established area of research in Geographic Information Science (GIS), but it is yet to feature in a Business Intelligence (BI) environment. Inclusion of this capability in BI can be achieved by modelling a utility network as a star schema. Star schema, which is a well-known data model used in data warehousing for BI solutions, organizes data into several fact tables referencing one or more dimension tables. This simplifies joins to provide fast execution of queries. Furthermore, star schema enables the analysis of data from multiple angles (slicing and dicing). However, modelling spatial data as star schema is still in its infancy for two main reasons, (a) only very recently have researchers started appreciating the importance of GIS capabilities in BI, (b) specific challenges associated with introducing geometry data with complex topological relationships into star schema. In this paper, we present a star schema to model geometric utility networks such as electricity, water and sewer systems. Our schema provides two-way benefits; it brings in an important new capability to BI in terms of spatial data analysis, and it gives non-technical users an opportunity carry out complex utility network analysis in an easy-to-use BI environment. Finally, we present an application of the star schema in service vulnerability assessment for electricity networks. Citation: Sadegholvaad, S., Wickramasuriya, R., Ma, J. & Perez, P. (2014). A Star Schema for Utility Network Analysis And Visualisation in a Geo-Business Intelligence Environment. In: Campbell P. and Perez P. (Eds), Proceedings of the International Symposium of Next Generation Infrastructure, 1-4 October 2013, SMART Infrastructure Facility, University of Wollongong, Australia

Postnatural urbanism in Jakarta: geosocial intelligence and the future of urban resilience

As cities evolve to become increasingly complex systems of people and interconnected infrastructure, the impacts of both extreme and long-term environmental change are significantly heightened. Understanding the resilience of urban systems and communities in an integrated manner is key to ensure the future sustainability of cities, which face considerable climatic, economic, and sociodemographic challenges in the 21st century. As Southeast Asia\u27s most populous and most dense metropolitan conurbation, and the second largest urban footprint in the world, Jakarta\u27s residents are exposed to rapid transformations of urban structures and systems

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 Vavilov\u27s 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