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

Local modelling: one size does not fit all

This editorial piece considers what happens when we abandon the concept that models of social processes have global application in favor of a local approach in which context or the influence of \u27place\u27 has an important role. A brief history of this local approach to statistical modelling is given, followed by a consideration of its ramifications for understanding societal issues. The piece concludes with futures challenges and prospects in this area

Compact Airborne Image Mapping System (CAIMS)

Airborne image mapping systems, to a large extent, remain the preserve of specialist aerial survey companies and research groups. This paper describes the current status of the CAIMS project, established in July 2006 at the National Centre of Geocomputation, National University of Ireland, Maynooth with their industrial partner; Compass Informatics, Dublin. It’s chief objective is to develop a compact, less complex, mobile airborne mapping system. Historically, aerial survey systems comprise technically complex and expensive image mapping systems. These high-end camera and navigation systems are usually installed in aircraft that have been specially adapted to carry out this activity. A dedicated full time team, including Survey Manager, Pilots and Observers are required to support this activity. Compounding the situation is the cost of advanced software modules and associated Data Processing specialists required to turn these data into useful georectified and orthorectified image products. Meanwhile, more advanced, less complex, reasonably priced imaging and navigation sensors continue to appear on the market. Allied to this trend are less complex, cheaper data processing modules enabling data to be collected and processed in a cost effective and timely manner. The CAIMS project was setup to review current technology for compact, relatively in-expensive, mobile aerial image mapping systems. The chief research objective was to develop a complete system in terms of survey operation, data acquisition and processing. Some secondary objectives include: (i) the development of a compact acquisition system that could be installed in common light-aircraft, using a removable, fully licensed mounting system; and (ii) the development of in-flight survey management software tools and downstream pre-processing modules enabling rapid turnaround of georectified mosaics. No attempt is made to reduce the role of conventional image survey systems but rather it is to look at areas where this new technology could be used to complement existing survey work and, indeed, open up new sectors. Some examples of the latter include development of rapid mobile aerial mapping methodologies and route corridor surveys. The results of this work will help develop novel solutions for some age-old aerial survey problems and so enable a wider audience access to this rapidly evolving technology

Calibrating spatial interaction models from GPS tracking data: an example of retail behaviour

Global Positioning System (GPS) technology has changed the world. We now depend on it for navigating vehicles, for route finding and we use it in our everyday lives to extract information about our locations and to track our movements. The latter use offers a potential alternative to more traditional sources of movement data through the construction of trip trajectories and, ultimately, the construction of origin-destination flow matrices. The advantage of being able to use GPS-derived movement data is that such data are potentially much richer than traditional sources of movement data both temporally and spatially. GPS-derived movement data potentially allow the calibration of spatial interaction models specific to very short time intervals, such as daily or even hourly, and for user-specified origins and destinations. Ultimately, it should be possible to calibrate continuously updated models in near real-time. However, the processing of GPS data into trajectories and then origin-destination flow matrices is not straightforward and is not well understood. This paper describes the process of transferring GPS tracking data into matrices that can be used to calibrate spatial interaction models. An example is given using retail behaviour in two towns in Scotland with an origin-constrained spatial interaction model calibrated for each day of the week and under different weather conditions (normal, rainy, windy). Although the study is small in terms of individuals and spatial context, it serves to demonstrate a future for spatial interaction modelling free from the tyranny of temporally static and spatially predefined data sets

Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

Spatial nonstationarity is a condition in which a simple ‘global” model cannot explain the relationships between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. In this paper, a technique is developed, termed geogra hically weighted regression, model which allows diferent relationships to exist at diferent points in space. This technique is loosely based on kernel regression. The method itself is introduced and related issues such as the choice of a spatial weighting function are discussed. Following this, a series of related statistical tests are considered which can be described generally as tests for spatial nonstationarity. Using Monte Carlo methods, techniques are proposed for investigatin the null non-stationa y one and also for testing whether individual regression coeficients are stable over geographic space. These techniques are demonstrated on a data set from the 1991 U. K. census relating car ownership rates to social class and mule unemployment. The paper concludes by discussing ways in which the technique can be extended

The geography of parameter space: an investigation of spatial non-stationarity

Abstract included in text

Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor

In this study, we link and compare the geographically weighted regression (GWR) model with the kriging with an external drift (KED) model of geostatistics. This includes empirical work where models are performance tested with respect to prediction and prediction uncertainty accuracy. In basic forms, GWR and KED (specified with local neighbourhoods) both cater for nonstationary correlations (i.e. the process is heteroskedastic with respect to relationships between the variable of interest and its covariates) and as such, can predict more accurately than models that do not. Furthermore, on specification of an additional heteroskedastic term to the same models (now with respect to a process variance), locallyaccurate measures of prediction uncertainty can result. These heteroskedastic extensions of GWR and KED can be preferred to basic constructions, whose measures of prediction uncertainty are only ever likely to be globallyaccurate. We evaluate both basic and heteroskedastic GWR and KED models using a case study data set, where data relationships are known to vary across space. Here GWR performs well with respect to the more involved KED model and as such, GWR is considered a viable alternative to the more established model in this particular comparison. Our study adds to a growing body of empirical evidence that GWR can be a worthy predictor; complementing its more usual guise as an exploratory technique for investigating relationships in multivariate spatial data sets

Gravity and Spatial Interaction Models

One of the major intellectual achievements and, at the same time, perhaps the most useful contribution by spatial analysts to social science literature is the development of gravity and spatial interaction models. This book provides an excellent and lucid introduction to the evolution of the gravity and spatial interaction models and their specification. These models are placed within the historical context of the development of the general spatial interaction literature. Haynes and Fotheringham outline the characteristics that have contributed to making these models among the most widely applied in forecasting and in general studies of migration, communications, transportation, and retailing, among other topics in urban and regional analysis. SCIENTIFIC GEOGRAPHY SERIES, Grant Ian Thrall, editor.https://researchrepository.wvu.edu/rri-web-book/1010/thumbnail.jp

Activity triangles : a new approach to measure activity spaces

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 PDFPeer reviewe

Role of Spatial Video in GIS

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