Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

Bridging the Gap Between Traditional Metadata and the Requirements of an Academic SDI for Interdisciplinary Research

Metadata has long been understood as a fundamental component of any Spatial Data Infrastructure, providing information relating to discovery, evaluation and use of datasets and describing their quality. Having good metadata about a dataset is fundamental to using it correctly and to understanding the implications of issues such as missing data or incorrect attribution on the results obtained for any analysis carried out. Traditionally, spatial data was created by expert users (e.g. national mapping agencies), who created metadata for the data. Increasingly, however, data used in spatial analysis comes from multiple sources and could be captured or used by nonexpert users – for example academic researchers ‐ many of whom are from non‐GIS disciplinary backgrounds, not familiar with metadata and perhaps working in geographically dispersed teams. This paper examines the applicability of metadata in this academic context, using a multi‐national coastal/environmental project as a case study. The work to date highlights a number of suggestions for good practice, issues and research questions relevant to Academic SDI, particularly given the increased levels of research data sharing and reuse required by UK and EU funders

Predictive spatial network analysis for high resolution transport modelling, applied to cyclist flows, mode choice and targeting investment

Betweenness is a measure long used in spatial network analysis (SpNA) to predict flows of pedestrians and vehicles, and more recently in public health research. We improve on this approach with a methodology for combining multiple betweenness computations using cross-validated ridge regression to create wide-scale, high-resolution transport models. This enables computationally efficient calibration of distance decay, agglomeration effects, and multiple trip purposes. Together with minimization of the Geoffrey E. Havers (GEH) statistic commonly used to evaluate transport models, this bridges a gap between SpNA and mainstream transport modeling practice. The methodology is demonstrated using models of bicycle transport, where the higher resolution of the SpNA models compared to mainstream (four-step) models is of particular use. Additional models are developed incorporating heterogeneous user preferences (cyclist aversion to motor traffic). Based on network shape and flow data alone the best model gives reasonable correlation against cyclist flows on individual links, weighted to optimize GEH (r2 = 0.78, GEH = 1.9). As SpNA models use a single step rather than four, and can be based on flow data alone rather than demographics and surveys, the cost of calibration is lower, ensuring suitability for small-scale infrastructure projects as well as large-scale studies

Visual Analytical Approaches to Evaluate Uncertainty and Bias in Crowdsourced Crisis Information

Increasing numbers of people are using social media to exchange information during crisis and conflict events. On the one hand, the humanitarian community is reluctant to use this information in the response effort as it fears the cost of untrustworthy and inaccurate information. On the other, the volunteer and technical communities have attempted to resolve this impasse by crowdsourcing crisis information; for example, by asking volunteers to ascertain whether a crisis report is trustworthy and accurate. Trust and accuracy are two characteristics of uncertainty: The fact that each is likely to have spatial, temporal and thematic aspects is supported by research, which suggests that geography characterises crisis information. Consequently, a research programme grounded in geographic information science, (geo)visualization and (geo)visual analytics is presented that seeks to evaluate the degree to which uncertainty and bias (systematic variation) are found in crowdsourced crisis information; and seeks to provide heuristics to help manage these factors. This programme consists of a methodology for undertaking interactive, analysis-guided software development that is informed by action research, scenario-based design and Munzner's model of visualization validation; and a prototype software application that combines interactive visual representations with spatial statistical functions to explore two datasets of crowdsourced crisis information. Following a review of the literature and a description of the data, the methodology and its implementation are placed within an appropriate work plan. Three supporting publications are included, as well as supporting statements regarding the author's skills and engagement with the academic community

Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end,this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.tgis_1197 283..29

The State of the Art in Cartograms

Cartograms combine statistical and geographical information in thematic maps, where areas of geographical regions (e.g., countries, states) are scaled in proportion to some statistic (e.g., population, income). Cartograms make it possible to gain insight into patterns and trends in the world around us and have been very popular visualizations for geo-referenced data for over a century. This work surveys cartogram research in visualization, cartography and geometry, covering a broad spectrum of different cartogram types: from the traditional rectangular and table cartograms, to Dorling and diffusion cartograms. A particular focus is the study of the major cartogram dimensions: statistical accuracy, geographical accuracy, and topological accuracy. We review the history of cartograms, describe the algorithms for generating them, and consider task taxonomies. We also review quantitative and qualitative evaluations, and we use these to arrive at design guidelines and research challenges

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local level will better enable the police to conduct proactive confidence interventions to meet the concerns of local communities. Conventional approaches do not consider that public confidence varies across geographic space as well as in time. Neighborhood level approaches to modeling public confidence in the police are hampered by the small number problem and the resulting instability in the estimates and uncertainty in the results. This research illustrates a spatiotemporal Bayesian approach for estimating and forecasting public confidence at the neighborhood level and we use it to examine trends in public confidence in the police in London, UK, for Q2 2006 to Q3 2013. Our approach overcomes the limitations of the small number problem and specifically, we investigate the effect of the spatiotemporal representation structure chosen on the estimates of public confidence produced. We then investigate the use of the model for forecasting by producing one‐step ahead forecasts of the final third of the time series. The results are compared with the forecasts from traditional time‐series forecasting methods like naïve, exponential smoothing, ARIMA, STARIMA, and others. A model with spatially structured and unstructured random effects as well as a normally distributed spatiotemporal interaction term was the most parsimonious and produced the most realistic estimates. It also provided the best forecasts at the London‐wide, Borough, and neighborhood level

Platial geo-temporal demographics using family names

We introduce platial geo-temporal demographics as a novel way to describe places using family names as markers of migration and change at sub-national scales. By identifying the likely origins of 59,218 surnames in Great Britain, we create platial profiles of surname mixes in terms of the distance their forbears have likely migrated between 1881 and 1998/2016. By combining individual-level data derived from historic censuses of population with near-complete contemporary population registers of enfranchised adults, we demonstrate how locally and regionally distinctive surname mixes can be used in characterizing places in terms of demographic change and stasis. The results suggest that a hierarchy of places arises in Great Britain, with larger conurbations (e.g., London and Birmingham) having more surnames that can be traced back to other parts of Great Britain and beyond, as opposed to places that are characterized by the presence of a larger share of surnames that have a more local origin. These regional differences are likely linked to processes of social mobility and economic activity

Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end,this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.tgis_1197 283..29