Mapping the results of local statistics

The application of geographically weighted regression (GWR) – a local spatial statistical technique used to test for spatial nonstationarity – has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity.geographically weighted regression, local statistics, mapping, nonstationarity

The importance of scale in spatially varying coefficient modeling

While spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the "spatial scale" of each data relationship is crucially important to make SVC modeling more stable, and in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of scale. Models are examined through a series of Monte Carlo simulation experiments to assess the extent to which spatial scale influences model stability and the accuracy of their SVC estimates. The following models are studied: (i) geographically weighted regression (GWR) with a fixed distance or (ii) an adaptive distance bandwidth (GWRa), (iii) flexible bandwidth GWR (FB-GWR) with fixed distance or (iv) adaptive distance bandwidths (FB-GWRa), (v) eigenvector spatial filtering (ESF), and (vi) random effects ESF (RE-ESF). Results reveal that the SVC models designed to capture scale dependencies in local relationships (FB-GWR, FB-GWRa and RE-ESF) most accurately estimate the simulated SVCs, where RE-ESF is the most computationally efficient. Conversely GWR and ESF, where SVC estimates are naively assumed to operate at the same spatial scale for each relationship, perform poorly. Results also confirm that the adaptive bandwidth GWR models (GWRa and FB-GWRa) are superior to their fixed bandwidth counterparts (GWR and FB-GWR)

Does regional development explain international youth mobility? Spatial patterns and global/local determinants of the recent emigration of young Italians

In this essay, we tackle the issue of the international mobility of young Italians in relation to regional disparities. Our intention is to determine if and to what extent a relationship exists between regional development and the international mobility of young people. We analyze the international migration of Italian citizens aged 15-34 who left the country in the period 2010-2017 using several variables that reflect the varying conditions found in different NUTS 3-level regions in terms of economic dynamism, labor-market efficiency, social fragility, educational underdevelopment and spatial peripherality. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models show that the international mobility of young Italians is very much dependent on local conditions and affected by spatial differences. It is greatest in the most economically dynamic areas of the country, in border regions and in metropolitan areas, with factors relating to spatial proximity and peripherality, imbalances in local labor markets, and paucity of human capital proving particularly significant

Geographic Information Science

This chapter begins with a definition of geographic information science (GIScience). We then discuss how this research area has been influenced by recent developments in computing and data-intensive analysis, before setting out its core organizing principles from a practical perspective. The following section reflects on the key characteristics of geographic information, the problems posed by large data volumes, the relevance of geographic scale, the remit of geographic simulation, and the key achievements of GIScience to date. Our subsequent review of changing scientific practices and the changing problems facing scientists addresses developments in high-performance computing, heightened awareness of the social context of geographic information systems (GISystems), and the importance of neogeography in providing new data sources, in driving the need for new techniques, and in heightening a human-centric perspective

Integration of INFOMAR Bathymetric Lidar with External Onshore Lidar Datasets

<br>This study was carried out by the National Centre for Geocomputation at NUI Maynooth. The NCG is a resource for those interested in any aspect of the capture, storage, integration, management, retrieval, display, analysis or modelling of spatial data. Research at the NCG is diverse, but much of its work focuses on Algorithm development, Geosensor integration, Geovisualisation and Location Based Services. Research under the Geosensors banner includes LiDAR research, which includes LiDAR acquisition, processing and error quantification.</br> <br>The primary aim of this study is to test onshore-to-offshore and offshore-to-onshore integration potential between INFOMAR bathymetric LiDAR data and onshore aerial LiDAR supplied by the Office of Public Works and Ordnance Survey Ireland. Three potential barriers to integration are examined and quantified (namely absolute LiDAR error, datum transformation error, and water-column segregation issues). Investigations focus on the potential for LiDAR integration in: • Sligo bay • Oranmore bay (within Galway bay) • Blennerville bay (within Tralee bay).</br&gt

The DIGMAP geo-temporal web gazetteer service

This paper presents the DIGMAP geo-temporal Web gazetteer service, a system providing access to names of places, historical periods, and associated geo-temporal information. Within the DIGMAP project, this gazetteer serves as the unified repository of geographic and temporal information, assisting in the recognition and disambiguation of geo-temporal expressions over text, as well as in resource searching and indexing. We describe the data integration methodology, the handling of temporal information and some of the applications that use the gazetteer. Initial evaluation results show that the proposed system can adequately support several tasks related to geo-temporal information extraction and retrieval

Detecting Stops from GPS Trajectories: A Comparison of Different GPS Indicators for Raster Sampling Methods

With the increasing prevalence of GPS tracking capabilities on smartphones, GPS trajectories have proven to be useful for an extensive range of research topics. Stop detection, which estimates activity locations, is fundamental for organizing GPS trajectories into semantically meaningful journeys. With previous methods overwhelmingly dependent on thresholds, contextual information or a pre-understanding of the GPS records, this paper addresses the challenge by contributing a ‘top-down’ raster sampling method which samples pre-calculated GPS indicators and clusters the raster cells with significantly different values as stops. We report a comparison of a set of precalculated GPS indicators with two baseline methods. By referencing a ground truth travel dairy, the raster sampling method demonstrates good and reliable capabilities on producing high accuracy, low redundancy and close proximity to the ground truth in three distinct travel use cases. This further indicates a good generic stop detection method

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