Empiricism and stochastics in cellular automaton modeling of urban land use dynamics

An increasing number of models for predicting land use change in regions of rapidurbanization are being proposed and built using ideas from cellular automata (CA)theory. Calibrating such models to real situations is highly problematic and to date,serious attention has not been focused on the estimation problem. In this paper, wepropose a structure for simulating urban change based on estimating land usetransitions using elementary probabilistic methods which draw their inspiration fromBayes' theory and the related ?weights of evidence? approach. These land use changeprobabilities drive a CA model ? DINAMICA ? conceived at the Center for RemoteSensing of the Federal University of Minas Gerais (CSR-UFMG). This is based on aneight cell Moore neighborhood approach implemented through empirical land useallocation algorithms. The model framework has been applied to a medium-size townin the west of São Paulo State, Bauru. We show how various socio-economic andinfrastructural factors can be combined using the weights of evidence approach whichenables us to predict the probability of changes between land use types in differentcells of the system. Different predictions for the town during the period 1979-1988were generated, and statistical validation was then conducted using a multipleresolution fitting procedure. These modeling experiments support the essential logicof adopting Bayesian empirical methods which synthesize various information aboutspatial infrastructure as the driver of urban land use change. This indicates therelevance of the approach for generating forecasts of growth for Brazilian citiesparticularly and for world-wide cities in general

Usability testing for improving interactive geovisualization techniques

Usability describes a product’s fitness for use according to a set of predefined criteria. Whatever the aim of the product, it should facilitate users’ tasks or enhance their performance by providing appropriate analysis tools. In both cases, the main interest is to satisfy users in terms of providing relevant functionality which they find fit for purpose. “Testing usability means making sure that people can find and work with [a product’s] functions to meet their needs” (Dumas and Redish, 1999: 4). It is therefore concerned with establishing whether people can use a product to complete their tasks with ease and at the same time help them complete their jobs more effectively. This document describes the findings of a usability study carried out on DecisionSite Map Interaction Services (Map IS). DecisionSite, a product of Spotfire, Inc.,1 is an interactive system for the visual and dynamic exploration of data designed for supporting decisionmaking. The system was coupled to ArcExplorer (forming DecisionSite Map IS) to provide limited GIS functionality (simple user interface, basic tools, and data management) and support users of spatial data. Hence, this study set out to test the suitability of the coupling between the two software components (DecisionSite and ArcExplorer) for the purpose of exploring spatial data. The first section briefly discusses DecisionSite’s visualization functionality. The second section describes the test goals, its design, the participants and data used. The following section concentrates on the analysis of results, while the final section discusses future areas of research and possible development

Modelling virtual urban environments

In this paper, we explore the way in which virtual reality (VR) systems are being broadened to encompass a wide array of virtual worlds, many of which have immediate applicability to understanding urban issues through geocomputation. Wesketch distinctions between immersive, semi-immersive and remote environments in which single and multiple users interact in a variety of ways. We show how suchenvironments might be modelled in terms of ways of navigating within, processes of decision-making which link users to one another, analytic functions that users have to make sense of the environment, and functions through which users can manipulate, change, or design their world. We illustrate these ideas using four exemplars that we have under construction: a multi-user internet GIS for Londonwith extensive links to 3-d, video, text and related media, an exploration of optimal retail location using a semi-immersive visualisation in which experts can explore such problems, a virtual urban world in which remote users as avatars can manipulate urban designs, and an approach to simulating such virtual worlds through morphological modelling based on the digital record of the entire decision-making process through which such worlds are built

Geography and computers: Past, present, and future

The discipline of Geography has long been intertwined with the use of computers. This close interaction is likely to increase with the embeddedness of computers and concomitant growth of spatially referenced data. To better understand the current situation, and to be able to better speculate about the future, this article provides two parallel perspectives: first, we offer an historical perspective on the relationship between Geography and computers; second, we document developments—in particular the nascent field of data science—that are currently taking place outside of Geography and to which we argue the discipline should be paying close attention. Combining both perspectives, we identify the benefits of tighter integration between Geography and Data Science and argue for the establishment of a new space—that we term Geographic Data Science—in which cross‐pollination could occur to the benefit of both Geography and the larger data community

Local spatiotemporal modeling of house prices: a mixed model approach

The real estate market has long provided an active application area for spatial–temporal modeling and analysis and it is well known that house prices tend to be not only spatially but also temporally correlated. In the spatial dimension, nearby properties tend to have similar values because they share similar characteristics, but house prices tend to vary over space due to differences in these characteristics. In the temporal dimension, current house prices tend to be based on property values from previous years and in the spatial–temporal dimension, the properties on which current prices are based tend to be in close spatial proximity. To date, however, most research on house prices has adopted either a spatial perspective or a temporal one; relatively little effort has been devoted to situations where both spatial and temporal effects coexist. Using ten years of house price data in Fife, Scotland (2003–2012), this research applies a mixed model approach, semiparametric geographically weighted regression (GWR), to explore, model, and analyze the spatiotemporal variations in the relationships between house prices and associated determinants. The study demonstrates that the mixed modeling technique provides better results than standard approaches to predicting house prices by accounting for spatiotemporal relationships at both global and local scales

Geocomputation and open source software: components and software stacks.

Geocomputation, with its necessary focus on software development and methods innovation, has enjoyed a close relationship with free and open source software communities. These extend from communities providing the numerical infrastructure for computation, such as BLAS (Basic Linear Algebra Subprograms),through language communities around Python, Java and others, to communities supporting spatial data handling, especially the projects of the Open Source Geospatial Foundation. This chapter surveys the stack of software components available for geocomputation from these sources, looking in most detail at the R language and environment, and how OSGeo projects have been interfaced with it. In addition, attention will be paid to open development models and community participation in software development. Since free and open source geospatial software has also achieved a successively greater presence in proprietary software as computational platforms evolve, the chapter will close with some indications of future trends in software component stacks, using Terralib as an example.Geocomputation; Open source software

A suggested framework and guidelines for learning GIS in interdisciplinary research

Interdisciplinary research with geographic information systems (GIS) can be rewarding as researchers from different disciplines have the opportunity to create something novel. GIS, though, is known to be difficult to use and learn. It is imperative for its successful use in projects that those who need to use GIS are able to learn it quickly and easily. To better support interdisciplinary research with GIS, it is necessary to understand what researchers with interdisciplinary experience wanted to use it for and how they learned it. The aim would be to advise geography educators on creating learning resources that could compliment or supplement existing learning approaches used by interdisciplinary researchers to improve the learning experience and uptake of GIS. This article explores the results from an online survey and interviews conducted between July 2014 and August 2015 with participants from the UK, the US and Europe on how interdisciplinary researchers learned GIS and which resources and platforms were utilised. Guidelines and a framework are presented, modifying the Technological Pedagogical and Content Knowledge framework, incorporating informal and context-based learning and GIS concepts from the Geographic Information Science and Technology Body of Knowledge. Findings show that interdisciplinary researchers want to use GIS to capture, analyse and visualise information; they largely use informal learning approaches (e.g. internet searches, watching a video, ask a more experienced person); and they predominantly use ArcGIS, QGIS and web GIS platforms. Future work suggests resources use contextually relevant learning activities and bear in mind nuances of disciplinary language

Key challenges in agent-based modelling for geo-spatial simulation

Agent-based modelling (ABM) is fast becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation tuned to London data which elaborates the standard Schelling (1971) model, and an agent-based residential location built according to spatial interactions principles, calibrated to trip data for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions