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Funding: This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994) and the ESRC Grant (grant number ES/L011921/1). There is an on-going challenge to describe, analyse and visualise the actual and potential extent of human spatial behaviour. The concept of an activity space has been used to examine how people interact with their environment and how the actual or potential spatial extent of individual spatial behaviour can be defined. In this paper we introduce a new method for measuring activity spaces. We first focus on the definitions and the applications of activity space measures, identifying their respective limitations. We then present our new method, which is based on the theoretical concept of significant locations, that is, places where people spent most of their time. We identify locations of significant places from GPS trajectories and define the activity space of an individual as a set of the first three significant places forming a so-called "activity triangle”. Our new method links the distances travelled for different activities to whether or not people group their activities, which is not possible using existing methods of measuring activity spaces. We test our method on data from a GPS-based travel survey across three towns is Scotland and look at the variations in size and shape of the designed activity triangle among people of different age and gender. We also compare our activity triangle with five other activity spaces and conclude by providing possible routes for improvement of activity space measures when using real human movement data (GPS data). Publisher PDF

A recent paper expands the well-known geographically weighted regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model—a framework referred to as multiscale GWR (MGWR). However, one limitation of the MGWR framework is that, until now, no inference about the local parameter estimates was possible. Formally, the so-called “hat matrix,” which projects the observed response vector into the predicted response vector, was available in GWR but not in MGWR. This paper addresses this limitation by reframing GWR as a Generalized Additive Model, extending this framework to MGWR and then deriving standard errors for the local parameters in MGWR. In addition, we also demonstrate how the effective number of parameters can be obtained for the overall fit of an MGWR model and for each of the covariates within the model. This statistic is essential for comparing model fit between MGWR, GWR, and traditional global models, as well as for adjusting multiple hypothesis tests. We demonstrate these advances to the MGWR framework with both a simulated data set and a real-world data set and provide a link to new software for MGWR (MGWR1.0) which includes the novel inferential framework for MGWR described here.

The research presented in this paper focuses on the analysis of spatial variations in house price adjustments due to economic conditions, and to quantify and describe patterns in the variations of house prices in the study area of Fife, Scotland. To show the impacts of national and local economic conditions on house price levels, specific methods of spatial analysis, such as Interpolation and Geographically Weighted Regression, are needed. The data for this analysis comes from the Registers of Scotland. The Registers of Scotland is responsible for compiling and maintaining registers relating to property and other legal documents. Their main aim is to record and safeguard rights (e.g. respond to policies adopted by the Scottish Government), whilst providing open and efficient access to important information. Results show that there are spatial variations in house prices, whereby the house prices in the north are on the rise, whereas in the south the hotspots seem to disappear.

The Irish famine of the 1840s had a dramatic effect both on the population within Ireland and the populations of countries such as the US, the UK and Australia, which received the bulk of the Irish diaspora resulting from the famine (Kenny 2003). As such, the effects of the famine have been examined extensively across a range of disciplines. It is therefore a challenge to provide any new perspective on this well-researched area. However, this paper provides novel insights into the spatial effects of the famine on population in two ways. Firstly, we present the most spatially detailed data recorded to date on population change in the period 1841–51 covering the famine. We are able to do this by, for the first time, linking census data from 1851 (which also records 1841 population) to the boundaries of 3436 Electoral Divisions (EDs) to provide a very detailed description of the uneven nature of population change during the famine decade. Secondly, by collecting data at the same spatial scale for over 100 other variables, we are able to analyse the relationship between population change during this decade and various demographic, locational and land use characteristics of EDs. We do this through not only traditional regression but also by geographically weighted regression (GWR), which allows us to investigate possible spatial variations in the determinants of population change during the famine period. The results of this analysis raise a series of intriguing new questions relating to the effect of the Irish famine on population change and point the way to further detailed historical and geographical research on this important topic. The research also demonstrates the use of GIS and spatial analytical techniques in historical geography as a means of uncovering new questions that can be answered by further qualitative research.

Principal Components Analysis (PCA) is a widely used technique in the social and physical sciences. Originally developed by Pearson (1901) , the details of extracting components for a data matrix were presented in an extensive two part pa per by Hotelling (1933). In this paper we examine some problems concerning the extraction and interpretation of geographically weighted principal components (Fotheringham et al. 2002: p196-202). We initially consider the basics of principal components, the development of locally weighted principal components (LWPCA) in the exploration of local subsets in attribute space, and finally geographically weighted principal components (GWPCA). As an illustration of the use and interpretation of GWPCA we demonstrate some interpretive methods in a study of social structure in Greater Dublin. A recent application of GWPCA is provided by Lloyd’s (2010) study of population ch aracteristics in Northern Ireland and we extend some of the methodology in this paper.

Geographically weighted regression (Fotheringham et al., 2002) is a method of modelling spatial variability in regression coefficients. The procedure yields a separate model for each spatial location in the study area with all models generated from the same data set using a differential weighting scheme. The weighting scheme, which allows for spatial variation in the model parameters, involves a bandwidth parameter which is usually deter- mined from the data using a cross-validation procedure. Part of the main output is a set of location-specific parameter estimates and associated t statistics which can be used to test hypotheses about individual model parameters. If there are n spatial locations and p parameters in each model, there will be up to np hypotheses to be tested which in most applications defines a very high order multiple inference problem. Solutions to problems of this type usually involve an adjustment to the decision rule for individual tests designed to contain the overall risk of mistaking chance variation for a genuine effect. An undesir- able by-product of achieving this control is a reduction in statistical power for individual tests, which may result in genuine effects going undetected. These two competing aspects of multiple inference have become known as the multiplicity problem. In this paper we develop a simple Bonferroni style adjustment for testing multiple hypotheses about GWR model coefficients. The adjustment takes advantage of the intrinsic dependency between local GWR models to contain the overall risk mentioned above, without the large sacrifice in power associated with the traditional Bonferroni correction. We illustrate this adjustment and a range of other corrective procedures on two data sets. The first models the determinants of educational attainment in the counties of Georgia USA. Using area based census data we examine the links between levels of educational attainment and four potential predictors: the proportion of elderly, the proportion who are foreign born, the proportion living below the poverty line and the proportion of ethnic blacks. The second model is a geographically weighted hedonic house price model based on individual mortgage records in Greater London in 1990. In both models we show how the various corrections can be used to guide the interpretation of the spatial variations in the parameter estimates. Finally we compare the statistical power of the proposed method with Bonferroni/Sidak corrections and those based on false discovery rate control.

GIS elemental units are often defined in terms of points, lines and areas. However, another type of spatial data that is becoming frequently captured, but as yet, GIS largely ignores, is that of spatial video. Here we consider the implementation of spatial video data within GIS.

Spatial Video is any video sequence that has spatial properties associated with it. This paper presents a conceptual model called a Viewpoint which is used to fully define a Spatial Video data set. We highlight how existing models of Spatial Video are limited by their disparate requirements and how our Viewpoint definition can be used in the general case. To do this we define a model that uses the GIS point and polygon data type primitives to create a hypothetically maximum spatial extent for each image contained within a Spatial Video sequence. This concept is shown to both restrict and extend these GIS primitives, but also provide the means to broaden Spatial Video’s exposure to a larger number of GIS geospatial analysis operations. Thus, with our conceptual model of Spatial Video as a Viewpoint moving object we can further develop our theories into practical data modelling examples and natural or formal language domains.

Public transportation plays an increasingly important role in building sustainable cities. However, a major deterrent to using public transport is the uncertainty of the length of waiting times at stops. This research suggests a solution based on location tracking based on an example of the use of tram transportation in Blackpool, north-west England. It utilises two mobile services: one involving real-time monitoring; the other involving dynamic scheduling. The former is intended for use by public transport managers. The later is intended for use by the general public. Both provide information on the current location of the next tram and its estimated arrival time. The system, TramInfo, was built by PHP and AJAX and the embedded application software was developed by J2ME based on Symbian OS. Future work is also discussed.

Global average temperatures have increased by about 0.6°C (± 0.2°C) during the 20th century, and are projected to increase by between 1.4°C and 5.8°C by 2100 (IPCC,2001a). This is predicted to lead to worldwide sea-level rise (SLR) of up to 1m by 2100 (IPCC, 2001b), which will have an impact on coastal environments protected under the Natura 2000 Habitats and Birds Directives (EU, 1992 & 1979).

Global average temperatures have increas ed by approximately 0.6°C during the 20 th century, and are projected to increase by a minimum additional 1.4 °C by 2100. The relationship between atmospheric warmi ng and sea-level rise (SLR) is well documented, and is expected to lead to SLR of up to 0.8m by 2100 (IPCC, 2007). Estuaries are predominantly sedimentary environments, and are characterised by shallow sloping gradients, making them particularly sensitive to SLR. The Shannon estuary is the largest river estuary in Ireland and is designated as a Special Area of Conservation, providing legisl ative protection for a range of habitat types, including estuarine salt marsh (E.U., 1992).

In spite of literature on hedonic house price models is quite vast, much more efforts must be made to integrate and to model spatiotemporal data (see Cressie, 1993). Nevertheless, it is worth mentioning that excellent works have been done in recent years to deal with this phenomenon. For example, of paricular interest has been the research on panel data models (Holly et al., 2006) and spatiotemporal autoregressive (STAR) models (Pace et al., 1998). On the other hand, the development of geographically weighted regression (Brunsdon et al., 1998) has brought new insights into the understanding of spatial dynamics in econometric models. In this respect, we do believe that the incorporation of temporal data into the model and the subsequent development of a spatiotemporal version of GWR will naturally contribute to a better understanding of stochastic processes that vary and interact in space and time. This is presicely the aim of this study, that is, to develop a spatiotemporal version of the GWR technique, which would enable the forecasting of and eventually the interpolation of local parameters throughout time.

Levels of co-operation between government bodies, semi-state agencies, community groups and private industry in Northern Ireland and the Republic of Ireland are increasing rapidly with respect to issues such as coordinated infrastructure planning, health care, trade and enterprise, as well as peace and reconciliation. These initiatives require joined-up thinking and detailed rationales for their formation and funding, which in turn are dependent on evidence-based arguments. However, at present, providing consistent, coherent and reliable evidence on a cross-border basis for use at different scales is extremely difficult due to issues of data interoperability across the jurisdictions. In other words, data are often incompatible and therefore difficult to compare. Addressing issues of data interoperability is crucial to successful cross-border, evidence-based initiatives.

Transportation networks are, typically, one of the most economic valuable resources for any nation requiring a large percentage of GDP to build and maintain. These route corridors attract their own unique set of spatial information requirements in terms of overall management including planning, engineering and operation. Various disciplines within a road management agency require high quality, spatial data of objects and features occurring along these networks from road infrastructure, sub-surface pavement condition through to modelling noise. This paper examines the integration of relatively novel sensor data against some pressing spatial information requirements for a small European road management agency. LiDAR systems are widely available and now used to record data from both aerial and terrestrial survey platforms. One of the chief LiDAR outputs are X,Y,Z points enabling a reliable 2.5-D geometric surface to be produced. Stereoscopic imagery is also collected from similar airborne and terrestrial mobile platforms. Both provide different datasets in terms of their respective optical and geometric properties. For example, stereoscopic cameras mounted on a survey vehicle record different data compared to LiDAR mounted near vertically on an airborne platform. Airborne LiDAR provides a more comprehensive geometric record whereas stereoscopic imagery can be used to provide a more comprehensive visual descriptor of the immediate route corridor. Acquisition systems for both sensors are relatively well understood and developed. Both systems collect large volumes of data that require a significant amount of data processing in order to produce useful information. A more efficient result can be achieved by integrating these two datasets within a GIS. The preliminary results of integration of airborne LiDAR with ground based stereo imaging systems are presented. How well this integration satisfies the growing spatial information requirements of the road agency are also examined.

In the aftermath of a disaster like Hurricane Katrina, remote-sensing methods are often employed in an effort to assess damage. However, their utility may be limited by the aerial perspective and image resolution. The Spatial Video Acquisition System (SVAS), in conjunction with a Geographic Information System (GIS), has the potential to be a complementary methodology for obtaining damage assessment information as well as capturing recovery related geographies associated with post-traumatic stress. An example is provided from the Lower 9th Ward of New Orleans with data that could be used to predict neighborhood post-traumatic stress. Results reveal six dimensions in which a SVAS can improve existing disaster-related data collection approaches: organization, archiving, transferability, evaluation, objectivity, and feasibility.

This text is written as a follow-up to a two-day workshop on Geographically Weighted Regression (GWR) held at the University of Leeds, June 2005. The aim of this text is both to introduce the reader to the basic concept of GWR through several empirical examples and also to demonstrate how to run GWR with software specifically written for this purpose. The software, GWR 3.0, is available from the authors and details can be found at: http://ncg.nuim.ie/GWR It is highly recommended that this software be used in conjunction with this text. It should be noted that GWR 3.0 produces a set of localised statistics that can be imported into other software for mapping.

Turnout in the 2002 general election confirm e d a downwar d trend in Irish voter pa rticipation levels, that first becam e evident in the early 1980s and was to culm inate in th e lowest turnout fo r an Irish general election since the foundation of the st ate (Figure 1). Given this context, voter turnout is a topic that requires further analysis within the Irish con t ex t, and two ap proaches to address i ng this top i c ex ists, with each approach having its own distin ctive adv a ntages and dis a dvantages. The surveying approach draws on individual-level data, and has the ad vantage of being able to draw on a wide range of variables, including social-psychological variables, an d m a ke direct inferences about the behaviour, and sources of the behaviour, of individuals. However, questionnaire surveys generally te nd to overestim ate turnout levels, with poten tial bias in su rvey re spon se rates resu lting in the under-rep res e ntation of non-voters in survey sam p les. The second type of data – aggregate data – is subject to problem s arising from the lim ited range of variables that can be accessed in seeking explanations of low turnout (u sually lim ited to data that can be drawn from census analyses) and the cross-lev e l infere nce problem that arises in any attem p t to m a ke inferences about individual-leve l behaviour from aggregate-leve l data (Achen and Shively, 1995). However, the big advantage of aggregate data lies in th e accuracy of the estim a tion of the dependent variable (turnout) and in the potential it offers for spatial and censu s-based analysis of variations in the turnout variable. Lower levels of aggregation allow fo r a larger num ber of cases to be analysed and for more detailed pictures of spatial varia tions in turnout leve ls to be gleaned.