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Oxx, K.; Brimicombe, A.J. and Rush, J. (2013) "Envisioning Deep Maps: Exploring the Spatial Navigation Metaphor in Deep Mapping" International Journal of Humanities and Arts Computing 7.1-2: 201-227

This study was competitively commissioned by the ESRC on behalf of the International Olympic Committee (IOC) and London 2012. Due to the open data policy and web dissemination of data tables, no new primary data collection was required to carry out the study. This is not the case with other host cities where in Vancouver and now in Sochi and Rio large amounts of primary data collection become necessary because fundamental data on the environment, economy and society are not readily available at sufficient granularity. The performance of London 2012 can continue to be monitored on an annual basis from open data updates which act as a barometer to legacy outcomes. This is a testament to the accessible time-series data infrastructure that has been created in the United Kingdom which for most data sets can be for a decade.

Brimicombe, A.J. and Li, Y.(2012) “Open Data and the Monitoring of the Sustainability of a London 2012 Legacy” Researching and Evaluating the Games Conference, London, Department for Culture Media and Sport (DCMS) of UK government.

This study was competitively commissioned by the ESRC on behalf of the International Olympic Committee (IOC) and London 2012. Due to the open data policy and web dissemination of data tables, no new primary data collection was required to carry out the study. This is not the case with other host cities where in Vancouver and now in Sochi and Rio large amounts of primary data collection become necessary because fundamental data on the environment, economy and society are not readily available at sufficient granularity. The performance of London 2012 can continue to be monitored on an annual basis from open data updates which act as a barometer to legacy outcomes. This is a testament to the accessible time-series data infrastructure that has been created in the United Kingdom which for most data sets can be for a decade.

Brimicombe, A.J. (2012) “Did GIS Start a Crime Wave? SatNav Theft and Its Implications for Geo-information Engineering” The Professional Geographer, (in press)

SatNavs are the first mass consumer product containing GIS and GPS technologies. The engineering of the product as an easily detachable device without login or other secured access meant that SatNavs quickly became a target of theft and imparted to the owners (at the time) an unrecognised level of vulnerability. Spatial clustering analyses show that SatNav thefts in London Borough Newham is significantly different to other thefts from vehicles reflecting in part visitor patterns and predatory, prolific offenders.

Li, Y and Brimicombe, A.J. (2011) "A New Variable for Spatial Accessibility Measurement in Social Infrastructure Planning" Proceedings 11th International Conference on GeoComputation, London, University College London (CD)

A new variable (Average Weighted Distance) is developed to measure and analyse spatial accessibility by small area geography. It will support rapid assessments of inequalities and ‘what-if’ analyses in local social infrastructure planning. The approach can use both Euclidean distance and network distance using postcode centroids as the atomic spatial unit. However, it is found that these two approaches have a high correlation and therefore similar patterns of relative inequality. The Euclidean distance approach has less computational load and is generally applicable, particularly where rapid ‘what-if’ analyses are required for decision support in a planning context. Local organisations are then able to interpret and further analyse relative local spatial accessibility for specific services/facilities as well as monitor changes in accessibility over time.

Brimicombe, A.J.; Li, Y. and Li, C. (2009) "Evidencing Population Change for Sustainable Community" Urban Design and Planning 162: 159-167

A hallmark of sustainable communities is their ability to adapt to demographic change. Fundamental to sustainable communities are the quality of services and opportunities afforded by the social infrastructure provision. Where the needs of residents change rapidly due to (im)migration, social and economic mobility and transience, there needs to be robust mechanisms of compiling and updating the evidence base on which policy and planning changes must necessarily be founded. A key component of such an evidence base is up-to the moment population estimates at small-area geographies. Current debates around the ability of official statistics to reflect actual population size and demographics, their lag in release and the geographical scale at which they are made available have prompted an investigation into a novel approach to population modelling using administrative data. This paper provides an insight into the population models for lower super output area (LSOA) level estimates developed for ten boroughs within the London Thames Gateway based on council tax, child benefit and schools census data. The multi-stage multiple regression models are initially constructed using 2001 data and tested against official statistics. The estimates are then moved forward with successive annual data sets to provide an understanding of year-on-year population change. This approach is not meant to displace official statistics but to provide another view through a different route; they data can be set alongside each other for evidential decision support in social infrastructure planning. This approach is now being applied, for example, by a number of Primary Care Trusts in growth areas in London and the south Midlands in order to inform decisions on health infrastructure planning.

Li, Y and Brimicombe, A.J. (2008) " Scenario-based Small Area Population Modelling for Social Infrastructure Planning" Proceedings GISRUK2008, Manchester: 348-353

In recent years, the geodemographic makeup of some areas in the UK has been rapidly changing. For example, immigration has put more pressure on child services, education and health care in places such as Slough, Peterborough and the Thames Gateway. Other factors affecting the Thames Gateway are housing development as part of the massive regeneration and the development and legacy of the Olympic site. This region is also experiencing high population churn, uncertainty in its demographic composition and issues in matching service delivery. There are also increasing demands for building sustainable communities that can adapt to change. A key to maintaining sustainable communities is the quality of services and opportunities afforded by the social infrastructure. Where the needs of residents rapidly change due to (im)migration, social and economic mobility and transience, there needs to be robust mechanisms for compiling and updating the evidence base on which policy and planning changes must necessarily be founded. This paper proposes scenario-base small area population modelling with multiple administrative data sources as a means of evidencing change. It is being implemented in the Thames Gateway London boroughs, with funding from UrbanBuzz (www.urbanbuzz.org) to support local social infrastructure planning.

Brimicombe, A.J. (2007) “Ethnicity, religion and residential segregation in London: evidence from a computational typology of minority communities” Environment & Planning B, Planning & Design 34: 904-924

Within the context of growing polarisation and fragmentation of the urban landscape, this paper presents a computational typology applicable to the study of minority communities, both ethnic and religious, useful in understanding their spatial distribution and juxtaposition at neighbourhood levels. The typology has been applied to multicultural London using the 2001 census in which there were questions on ethnicity and religion. The landscape of religion is found to be more highly segregated in contrast to the landscape of ethnicity. Furthermore, on the basis of a preliminary analysis of indicator variables, minorities seem on aggregate to be in an improved situation given a level of residential segregation with the exception of residents of segregated Asian-Bangladeshi areas for ethnicity and residents of segregated Muslim areas for religion. This questions the generally held view that segregation in a multicultural society is undesirable per se and suggests that a ‘one size fits all’ government policy towards residential segregation is insufficiently perceptive. The typology introduced here should facilitate a more critically informed approach to multiculturalism and the contemporary city.

Brimicombe, A.J. (2007) "A dual approach to cluster discovery in point event data sets" Computers, Environment and Urban Systems 31: 4-18

Spatial data mining seeks to discover meaningful patterns in data where a prime dimension of interest is geographical location. Consideration of a spatial dimension becomes important where data either refer to specific locations and/or have significant spatial dependence which needs to be considered if meaningful patterns are to emerge. For point event data there are two main groups of approaches to identifying clusters. One stems from the statistical tradition of classification which assigns point events to a spatial segmentation. A popular method is the k-means algorithm. The other broad approach is one which searches for ‘hot spots’ which can be loosely defined as a localised excess of some incidence rate. Examples of this approach are GAM and kernel density estimation. This paper presents a novel variable resolution approach to ‘hot spot’ cluster discovery which acts to define spatial concentrations within the point event data. ‘Hot spot’ centroids are then used to establish additional distance variables and initial cluster centroids for a k-means classification that produces a segmentation, both spatially and by attribute. This dual approach is effective in quickly focusing on rational candidate solutions to the values of k and choice of initial candidate centroids in the k-means clustering. This is demonstrated through the analysis of a business transactions database. The overall dual approach can be used effectively to explore clusters in very large point event data sets.

Brimicombe, A.J. (2005) "Cluster detection in point event data having tendency towards spatially repetitive events." Proceedings 8th International Conference on GeoComputation, Ann Arbor, University of Michigan (CD)

The analysis of point event patterns in geography, ecology and epidemiology have a long tradition. Of particular interest are patterns of clustering or 'hot spots' and such cluster detection lies at the heart of spatial data mining. Certain classes of point event patterns exhibit a tendency towards spatial repetitiveness (within the resolution of geo-positioning) although with a temporal separation. Examples are crime and traffic accidents. Spatial superimposition of point events challenges many existing approaches to cluster detection. In this paper a variable resolution approach, Geo-ProZones, is applied to residential burglary data exhibiting a high level of repeat victimisation. This is coupled with robust normalisation as a means of consistently defining and visualising the 'hot spots'. 

Brimicombe, A.J. (2005) "La détection des concentrations des evénements ponctuels ayant des répétitions spatiales" Actes du Colloque International de Géomatique et d'Analyse Spatiale, Avignon, France ISBN 2-910545-06-7 (CD)

Brimicombe, A.J. (2003) "A variable resolution approach to cluster discovery in spatial data mining" in Computational Science and Its Applications (eds. Kumar et al.), Springer-Verlag, Berlin, Vol. 3: 1-11 

Spatial data mining seeks to discover meaningful patterns from data where a prime dimension of interest is geographical location. Consideration of a spatial dimension becomes important when data either refer to specific locations and/or have significant spatial dependence which needs to be considered if meaningful patterns are to emerge. For point data there are two main groups of approaches. One stems from traditional statistical techniques such as k-means clustering in which every point is assigned to a spatial grouping and results in a spatial segmentation. The other broad approach searches for 'hotspots' which can be loosely defined as a localised excess of some incidence rate. Not all points are necessarily assigned to clusters. This paper presents a novel variable resolution approach to cluster discovery which acts in the first instance to define spatial concentrations within the data thus allowing the nature of clustering to be defined. The cluster centroids are then used to establish initial cluster centres in a k-means clustering and arrive at a segmentation on the basis of point attributes. The variable resolution technique can thus be viewed as a bridge between the two broad approaches towards knowledge discovery in mining point data sets. Applications of the technique to date include the mining of business, crime, health and environmental data.

Brimicombe, A.J. (2002) "Cluster discovery in spatial data mining: a variable resolution approach" in Data Mining III (eds. Zanasi et al.), WIT Press, Southampton: 625-634 

Spatial data mining seeks to discover meaningful patterns from data where a key dimension of the data is geographical location. This spatial dimension becomes important when data either refer to specific locations and/or have significant spatial dependence and which needs to be taken into consideration if meaningful patterns are to emerge. For point data there are two main groups of approaches. One stems from traditional statistical techniques such as k-means clustering in which every point is assigned to a spatial grouping and results in a spatial segmentation. The segmentation has k sub-regions, is usually space filling and non-overlapping (i.e. a tessellation) in which all points fall within a spatial segment. The difficulty with this approach is in defining k centroid locations at the outset of any data mining. The other broad approach searches for 'hotspots' which can be loosely defined as a localised excess of some incidence rate. In this approach not all points are necessarily assigned to clusters. It is the mainstay of those approaches which seek to identify any significantly elevated risk above what might be expected from an at-risk background population. Definition of the population at risk is clearly critical and in some data mining applications is not possible at the outset. This paper presents a novel variable resolution approach to cluster discovery which acts in the first instance to define spatial concentrations in the absence of population at risk. The cluster centroids are then used to establish initial centroids for techniques such as k-means clustering and arrive at a segmentation on the basis of point attributes. The variable resolution technique can thus be viewed as a bridge between the two broad approaches towards knowledge discovery in mining point data sets. The technique is equally applicable to the mining of business, crime, health and environmental data. A business-oriented case study is presented here.

Brimicombe, A.J.; Ralphs, M.; Sampson, A. and Tsui, P. (2001) "An exploratory analysis of the role of neighbourhood ethnic composition in the geographical distribution of racially motivated incidents" British Journal of Criminology 41: 293-308 

This paper explores the use of statistical and Geographical Information Systems mapping techniques in producing a preliminary assessment of geographical patterns of racially motivated crimes and harassment in a given area. The geographical distribution of allegations of racially motivated incidents reported to the police in the London Borough of Newham is investigated. The results of the analysis suggest that the ethnic composition of an area appears to have a significant effect on the rate of incidents. Correlation and regression analyses are carried out and support the preliminary finding that rates of incidence are significantly higher where there is a large white majority and smaller groups of other ethnicities.

Brimicombe, A.J. and Tsui, P. (2000) "A variable resolution, geocomputational approach to the analysis of point patterns" Hydrological Processes 14: 2143-2155 

A geocomputational approach to the solution of applied spatial problems is being ushered in to take advantage of ever increasing computer power. The move is seen widely as a paradigm shift allowing better solutions to be found for old problems, solutions to be found for previously unsolvable problems and the development of new quantitative approaches to geography. This paper uses geocomputation to revisit point pattern analysis as an objective, exploratory means of evaluating mapped distributions of landforms and/or events. A new variable resolution approach is introduced and tested alongside more traditional approaches of nearest neighbour distance and quadrat analysis and against another geocomputational approach, the K function. The results demonstrate that firstly, the geocomputational paradigm allows new and more useful solutions to be found for old problems. Secondly, a variable resolution approach to geographical data analysis goes some way towards overcoming the problem of scale inherent in such analyses. Finally, the technique facilitates spatio-temporal analyses of event data, such as landslides, thus offering new lines of enquiry in areas such as hazard mitigation.

Brimicombe, A.J. (2000) "Constructing and evaluating contextual indices using GIS: a case of primary school performance" Environment & Planning A 32: 1909-1933 

The current political agenda has a firm focus on primary school education as one, among a number of critical public services, that determine electorate opinion. Performance tables which emphasise aggregate examination scores have become an entrenched feature of the educational landscape for parents, teachers and policy makers. Yet it is widely accepted that these types of tables of aggregate examination scores provide a problematic, even flawed guide to the performance of schools. Given the recognised broad link between pupil performance and the social and economic environment in which they live and are brought up, there is continued interest in being able to contextualise school examination scores so as to better reflect relative achievement. Inequalities are inherently spatial phenomena and with the use of census-based indices to measure them, it is not surprising that geographical information systems (GIS) are increasingly being used in the task of creating contextual measures. This paper explores a methodology for creating and analysing a contextual index of ambient disadvantage centred on robust normalisation of data and is illustrated using census variables, pupil numbers and test scores for 3687 primary schools in the north of England. Relevant census variables are interpolated using ordinary kriging with an element of smoothing so as to simulate to some extent the effect of school catchment areas. Key features of using robust normalisation are that variable weights can be tested and the internal level of support for an index, weighted absolute deviation, can be calculated and mapped. This latter provides a quality measure for an index. The methodology is critically assessed in relation to other recent approaches.

Brimicombe, A.J. (1999) "Small may be beautiful - but is simple sufficient?" Geographical and Environmental Modelling 3: 9-33 

The title of this paper is inspired by two trends in spatial analysis: a shift in perspective from global to local, and the growing sophistication of analytical techniques employed to do so. While simple techniques tend to be trivialized, they are robust without being brittle. Thus, this paper proposes a modified, robust tool for exploratory spatial data analysis - the normalized boxplot - alongside other robust measures of distribution. This tool can used to explore both the presence of spatial non-stationarity at the level and to recognize zones at the local level within which adequate spatial stationarity exists to develop meaningful hypotheses concerning causal relationships. The method is used in a case study of limiting long-term illness in 595 wards in North-East England. The analysis results, through the detection of spatial non-stationarity, in a spatial partitioning of the area into five locality types for which different relationships for the possible explanatory variables exist. Thus, the detection, description and explanation of spatial differentiation at the local level is clearly an important goal of spatial analysis, but as is shown through tools such as normalized boxplots, some simple, safe and easily understandable approaches are adequate to the task.[

Tsui, P. and Brimicombe, A.J. (1997) "Hierarchical tessellations model and its use in spatial analysis" Transactions in GIS 2: 267-279 

Hierarchical tessellation model is a class of spatial data models based on recursive decomposition of space. Quadtree is one such tessellation and is characterised by square cells and 1:4 decomposition ratio. To relax these constraints in tessellation, a generalised hierarchical tessellation data model, called Adaptive Recursive Tessellations (ART), has been proposed. ART increases flexibility in tessellation by the use of rectangular cells and variable decomposition ratios. In ART, users can specify cell sizes which are intuitively meaningful to their applications, or can reflect the scales of data. ART is implemented in a data structure, called Adaptive Recursive Run-Encoding (ARRE), which is a variant of two-dimensional run-encoding whose running path can vary with different tessellation structures of ART model. Given the recognition of the benefits of implementing statistical spatial analysis in GIS, the use of hierarchical tessellation models, such as ART, in spatial analysis are discussed. Firstly, ART can be applied to solve quadrat size problem in quadrat analysis for point pattern with variable size quadrats. Besides, ART can also act as data model in variable resolution block kriging technique for geostatistical data to reduce variation in kriging error. Finally, ART model can facilitate the evaluation of spatial autocorrelation for area data at multiple map resolutions and how to construct connectivity matrix for calculating spatial autocorrelation indices based on ARRE is also illustrated.

Tsui, P. and Brimicombe, A.J. (1996) "Hierarchical tessellations model and its use in spatial analysis" First International Conference on Geocomputation, Leeds, Vol2: 815-825

Quadtree, a classical hierarchical tessellation model, has been widely adopted as a GIS spatial data model for its ability to compress raster data. Nevertheless, due to its rigid tessellation structure, a generalised hierarchical tessellation model, called Adaptive Recursive Tessellations (ART) is proposed. It has a more flexible tessellation structure than quadtree as a result of the use of rectangular cells and variable decomposition ratios. Thus users can assign specific sizes of cells to different levels of an ART model which are intuitively useful or meaningful to their applications, or can reflect the scales of data. ART is represented by a special type of two-dimensional run-encoding, Adaptive Recursive Run-Encoding (ARRE), whose running path can vary with the tessellation structure of an individual ART model. The benefits of implementing statistical spatial analysis in GIS have been recognised. As a spatial data model, the use of hierarchical tessellation models in spatial analysis are discussed in this paper. The formulae for mean and variance statistics in terms of addresses of ARRE are stated. A connectivity matrix which is an essential element in calculating spatial autocorrelation can also be constructed based on the ARRE. Finally, the application of the ART model in solving modifiable areal unit problem is discussed. Flexibility of the tessellation structure and the inherent ability in spatial aggregation of the ART model have been found useful in studying scale and aggregation effects of areal units on spatial analysis.