Andrew J. Evans

Web-based public participation geographical information systems: an aid to local environmental decision-making

Current research examining the potential of the World-Wide Web as a means of increasing public participation in local environmental decision making in the UK is discussed. The paper considers traditional methods of public participation and argues that new Internet-based technologies have the potential to widen participation in the UK planning system. Evidence is provided of the potential and actual benefits of online spatial decision support systems in the UK through a real environmental decision support problem in a village in northern England. The paper identifies key themes developing in this area of Web-based geographical information systems (GIS) and provides a case-study example of an online public participation GIS from inception to the final phase in a public participation process. It is shown that in certain UK planning problems and policy formulation processes, participatory online systems are a useful means of informing and engaging the public and can potentially bring the public closer to a participatory planning system.

Public participation, GIS, and cyberdemocracy: evaluating on-line spatial decision support systems

In this paper we describe the development of Internet-based approaches to public participation and on-line spatial decision support systems in particular. Two case studies in developing web-based public participation GIS (PPGIS), one local and one regional, are described in detail. Results from the live testing of these systems are shown. These are discussed in the light of recent developments in ‘cyberdemocracy’ and conclusions are drawn about principles of on-line PPGIS and problems associated with public participation, user interaction, and familiarity with IT, copyright issues, access to the Internet, and relevant political structures.

Democratic input into the nuclear waste disposal problem: The influence of geographical data on decision making examined through a Web-based GIS

Abstract.This paper elucidates the manner in which users of an online decision support system respond to spatially distributed data when assessing the solution to environmental risks, specifically, nuclear waste disposal. It presents tests for revealing whether users are responding to geographical data and whether they are influenced by their home location (Not in My Back Yard – style behavior). The tests specifically cope with problems associated with testing home-to-risk distances where both locations are constrained by the shape of the landmass available. In addition, we detail the users’ wider feelings towards such a system, and reflect upon the possibilities such systems offer for participatory democracy initiatives.

A Hybrid Multi-Agent/Spatial Interaction Model System for Petrol Price Setting

This paper examines the use of multi-agent simulations (MAS) to model the petrol market. The development of a purely agent based model for petrol prices is presented. Failings within this model are discussed and an alternative strategy for controlling the price of each petrol station based on population of customers is considered. Individual level modelling of customers is too computationally intensive; therefore a spatial interaction model was used to estimate the sales and linked to the agent system to create a hybrid model. To evaluate how effective this hybrid model was, a comparison was made with an existing data set of real petrol prices collected over a two month period. This was achieved both statistically and visually with the use of a Geographical Information System (GIS). Experimentation revealed that the hybrid model outperformed the agent model. Investigation into the behaviour and sensitivity of the system (for example, how prices diffuse spatially) was undertaken by means of idealised simulations.

Genetic Algorithm Optimisation of An Agent-Based Model for Simulating a Retail Market:

Traditionally, researchers have used elaborate regression models to simulate the retail petrol market. Such models are limited in their ability to model individual behaviour and geographical influences. Heppenstall et al presented a novel agent-based framework for modelling individual petrol stations as agents and integrated important additional system behaviour through the use of established methodologies such as spatial interaction models. The parameters for this model were initially determined by the use of real data analysis and experimentation. This paper explores the parameterisation and verification of the model through data analysis and by use of a genetic algorithm (GA). The results show that a GA can be used to produce not just an optimised match, but results that match those derived by expert analysis through rational exploration. This may suggest that despite the apparent nonlinear and complex nature of the system, there are a limited number of optimal or near optimal behaviours given its constraints, and that both user-driven and GA solutions converge on them.

Implementing comprehensive offender behaviour in a realistic agent-based model of burglary

Explaining and modelling crime patterns is an exercise that has taxed policy-makers, criminologists, social reformers and the police ever since the first crime patterns were recorded. Crime is a particularly difficult phenomenon to model because of its inherent complexity; crime patterns are built up from a multitude of human-human and human-environment micro-interactions that ultimately lead to individual crime events. Commonly used modelling techniques, such as regression, struggle to fully account for the dynamics of the crime system. They work at aggregate scales thereby disregarding important individual-level variation and also struggle to account for the effects of different types of human behaviour. Furthermore, important concepts from environmental criminology – such as individual offender awareness spaces or heterogeneity in offender decision-making – cannot be included directly when working at a resolution above that of the individual. This research addresses the drawbacks associated with traditional mathematical crime models by building an agent-based simulation with a unique offender behavioural model. Through use of the PECS framework for modelling human behaviour, agents are endowed with needs and motives that drive their behaviour and ultimately lead to the commission of crime. As the model uses real-world environmental data, it can be used to make predictions in existing cities. The paper demonstrates that use of this framework, in combination with an agent-based model, can replicate patterns and trends that are supported by the current theoretical understanding of offending behaviour.

Developing and Testing an Online Tool for Teaching GIS Concepts Applied to Spatial Decision-Making.

The development and testing of a Web‐based GIS e‐learning resource is described. This focuses on the application of GIS for siting a nuclear waste disposal facility and the associated principles of spatial decision‐making using Boolean and weighted overlay methods. Initial student experiences in using the system are analysed as part of a research project on teaching GIS concepts to large numbers of students with little or no prior GIS experience. Some general thoughts on the utility of Web‐based GIS for learning and teaching are presented. Results from the first cohort of 167 undergraduate/postgraduate geography students using the system indicate that students find it easy to use, a useful aid to learning about the issues involved, and a thought‐provoking exercise in Internet‐based democracy.

An Agent-Based Model of Burglary

Occurrences of crime are complex phenomena. They are the result of a large number of interrelated elements which can include environmental factors as well as complex human behaviours. Traditionally, crime occurrences have been modelled using statistical techniques, and although such approaches are useful, they face difficulties in providing predictive analyses and with the integration of behavioural information. Also, it is particularly difficult to account for the strongly influential effect of local urban form. Agent-based modelling is a relatively new modelling paradigm that has generated a considerable amount of interest. An agent is an independent component of a system which interacts with other agents and its environment to achieve goals. In this manner, large systems of agents can be created to mimic real scenarios. Most importantly, the agents can incorporate behavioural information to determine how they should achieve their goals, and models can include a highly detailed environment. This paper presents an agent-based model used to predict burglary rates, which, despite its simplicity, yields interesting results. We apply the model to the city of Leeds, UK. The model indicates that the urban configuration in Leeds is a major element in determining the level of crime across the city. It also demonstrates that agent-based modelling is an excellent tool for these types of analyses with much potential.

Explaining value chain differences in MRIO databases through structural path decomposition

ABSTRACT Many multiregional input–output (MRIO) databases are used to calculate consumption-based accounts. Results feature in climate policy discussion on emissions reduction responsibilities; yet studies show that outcomes produced by each database differ. This paper compares the emissions associated with value chains from Eora, EXIOBASE, GTAP and WIOD. Structural path analysis identifies the largest paths in each database and the differences in common paths are calculated. For the top 100 value chain paths that contain the largest difference, structural path decomposition is used to identify the contribution each part of the value chain makes towards the difference. The results identify and quantify key flows that are the cause of difference in the databases. From these, we can conclude that key MRIO database construction decisions, such as using the residence or territorial principle for emissions allocation and whether energy spends are reallocated based on physical data, are the major causes of differences.

Agent-Based Modelling of Residential Mobility, Housing Choice and Regeneration

Dynamics in the housing market can be simulated using agent-based modelling. Focusing on the theme of urban regeneration, we present a housing market model framework which explores the causal relationships that occur in this market.