Road distance and travel time for spatial urban modelling

Interactions within and between urban environments include the price of houses, the flow of traffic and the intensity of noise pollution, which can all be restricted by various physical, regulatory and customary barriers. Examples of such restrictions include buildings, one-way systems and pedestrian crossings. These constrictive features create challenges for predictive modelling in urban space, which are not fully captured when proximity-based models rely on the typically used Euclidean (straight line) distance metric. Over the course of this thesis, I ask three key questions in an attempt to identify how to improve spatial models in restricted urban areas. These are: (1) which distance function best models real world spatial interactions in an urban setting? (2) when, if ever, are non-Euclidean distance functions valid for urban spatial models? and (3) what is the best way to estimate the generalisation performance of urban models utilising spatial data? This thesis answers each of these questions through three contributions supporting the interdisciplinary domain of Urban Sciences. These contributions are: (1) the provision of an improved approximation of road distance and travel time networks to model urban spatial interactions; (2) the approximation of valid distance metrics from non-Euclidean inputs for improved spatial predictions and (3) the presentation of a road distance and travel time cross-validation metric to improve the estimation of urban model generalisation. Each of these contributions provide improvements against the current state-of-the-art. Throughout, all experiments utilise real world datasets in England and Wales, such datasets contain information on restricted roads, travel times, house sales and traffic counts. With these datasets, I display a number of case studies which show up to a 32% improved model accuracy against Euclidean distances and in some cases, a 90% improvement for the estimation of model generalisation performance. Combined, the contributions improve the way that proximity-based urban models perform and also provides a more accurate estimate of generalisation performance for predictive models in urban space. The main implication of these contributions to Urban Science is the ability to better model the challenges within a city based on how they interact with themselves and each other using an improved function of urban mobility, compared with the current state-of-the-art. Such challenges may include selecting the optimal locations for emergency services, identifying the causes of traffic incidents or estimating the density of air pollution. Additionally, the key implication of this research on geostatistics is that it provides the motivation and means of undertaking non-Euclidean based research for non-urban applications, for example predicting with alternative, non-road based, mobility patterns such as migrating animals, rivers and coast lines. Finally, the implication of my research to the real estate industry is significant, in which one can now improve the accuracy of the industry's state-of-the-art nationwide house price predictor, whilst also being able to more appropriately present their accuracy estimates for robustness

Thirty Years of Spatial Econometrics

In this paper, I give a personal view on the development of the field of spatial econometrics during the past thirty years. I argue that it has moved from the margins to the mainstream of applied econometrics and social science methodology. I distinguish three broad phases in the development, which I refer to as preconditions, takeoff and maturity. For each of these phases I describe the main methodological focus and list major contributions. I conclude with some speculations about future directions.

A flexible multivariate conditional autoregression with application to road safety performance indicators.

There is a dearth of models for multivariate spatially correlated data recorded on a lattice. Existing models incorporate some combination of three correlation terms: (i) the correlation between the multiple variables within each site, (ii) the spatial autocorrelation for each variable across the lattice, and (iii) the correlation between each variable at one site and a different variable at a neighbouring site. These may be thought of as correlation, spatial autocorrelation and spatial cross-correlation parameters respectively. This thesis develops a exible multivariate conditional autoregression model where the spatial cross-correlation is asymmetric. A comparison of the performance of the FMCAR with existing MCARs is performed through a simulation exercise. The FMCAR compares well with the other models, in terms of model fit and shrinkage, when applied to a range of simulated data. However, the FMCAR out performs all of the existing MCAR models when applied to data with asymmetric spatial crosscorrelations. To demonstrate the model, the FMCAR model is applied to road safety performance indicators. Namely, casualty counts by mode and severity for vulnerable road users in London, taken from the STATS19 dataset for 2006. However, by exploiting correlation between multiple performance indicators within local authorities and spatial auto and cross-correlation for the variables across local authorities, the FMCAR results in considerable shrinkage of the estimates of local authority performance. Whilst this does not enable local authorities to be differentiated based upon their road safety performance it produces a considerable reduction in the uncertainty surrounding their rankings. This is consistent with previous attempts to improve performance rankings. Further, although the findings of this thesis indicate that there is only mild evidence of asymmetry in the spatial cross-correlations for road casualty counts, the thesis provides a demonstration of the applicability of this model to real world social and economic problems

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Spatial reallocation of areal data - a review

The analysis of socio-economic data often implies the combination of data bases originating from different administrative sources so that data have been collected on several separate partitions of the zone of interest into administrative units. It is therefore necessary to reallocate the data from the source spatial units to the target spatial units. We propose a review of the literature on statistical methods of spatial reallocation rules (spatial interpolation). Indeed one can distinguish several types of reallocation depending on whether the initial data and the final output are areal data or point data. We concentrate here on the areal-to-areal change of support case when initial and final data have an areal support with a particular attention to disaggregation for continuous data. There are three main types of such techniques: proportional weighting schemes also called dasymetric methods, smoothing techniques and regression based interpolation

Spatial reallocation of areal data - a review

The analysis of socio-economic data often implies the combination of data bases originating from different administrative sources so that data have been collected on several separate partitions of the zone of interest into administrative units. It is therefore necessary to reallocate the data from the source spatial units to the target spatial units. We propose a review of the literature on statistical methods of spatial reallocation rules (spatial interpolation). Indeed one can distinguish several types of reallocation depending on whether the initial data and the final output are areal data or point data. We concentrate here on the areal-to-areal change of support case when initial and final data have an areal support with a particular attention to disaggregation for continuous data. There are three main types of such techniques: proportional weighting schemes also called dasymetric methods, smoothing techniques and regression based interpolation

Management of intrinsic quality characteristics for high-value specialty coffees of heterogeneous hillside landscapes

Tropical hillsides are ecologically and socially diverse with a multitude of small- to medium-sized farms that offer a potential treasure chest of high-value market crops. Specialty coffees, for example, earn a substantial price premium and are therefore a promising opportunity for farmers. Coffee quality is determined by the natural environment and farm management practices. To sell high-priced coffee, farmers must produce beans desired by consumers who are willing to pay more for specific quality profiles. A targeting of the production practices to suit the continuously-changing market demands is necessary; the focus must be on controlling the processes that determine the quality characteristics. The present research aimed to develop a framework to manage the intrinsic coffee quality of heterogeneous hillside landscapes. In a two-tiered approach, firstly spatial prediction models were developed and tested to identify the comparative advantage of environmental niches and secondly systematic farm management practices were developed and tested to turn the comparative advantage of farmers into a competitive advantage. Commercial sensorial data of the two Colombian departments of Cauca and Antioquia, of the Veracruz department in Mexico and of the five coffee growing regions in Honduras were used to develop and test the framework. The results suggest that the framework is highly viable; the information generated is highly novel, is high-medium actionable and is medium deliverable to stakeholders. The specific conclusions derived are: (1) The production environment of coffee (natural environment, agronomic management and post-harvest processes) is variable over space. (2) Beverage quality of coffee is dependent on the production environment. The combination of decisive quality factors varies from location to location, and so does the contribution of each factor. (3) Production factors can be identified and their impact quantified. Subsequently the factors can be systematically controlled and managed to improve product quality. (4) Site-specific systematic and cyclic quality control processes are required to decrease produce variability and deliver a product sought by the market. (5) The approach is twofold, firstly the identification of suitable environmental niches followed by definition of site-specific management. (6) Farm management interventions are not always statistically significant but often relevant for farmers. (7) Qualitative quality-control methods using commercial data are viable indicators for quality measurements so long as consistent, skilled evaluators (cuppers) are selected in preliminary testing.Management der intrinsischen Qualitätscharakteristiken von hochwertigen Spezialitätenkaffees aus heterogenen Hanglagen Der Kaffeeanbau in tropischen Hanglagen variiert ökologisch sehr stark und ist sozial besonders geprägt durch eine Vielzahl von kleinen und mittleren landwirtschaftlichen Betrieben, welche ein hohes Potential für die Produktion von hochwertigen Agrarprodukten haben. Spezialitätenkaffees werden mit einem Mehrwert belohnt und sind deshalb eine vielversprechende Option für diese Bauern. Kaffeequalität ist wesentlich durch die natürlichen Umweltbedingungen und die agronomischen Praktiken bestimmt. Um hochwertige Kaffees vermarkten zu können, müssen die Bauern einen Rohkaffee produzieren, welcher vom Markt nachgefragt wird und für welchen der Konsument bereit ist, einen entsprechenden Aufpreis zuzahlen. Deshalb ist eine kontrollierte gezielte Produktion notwendig um mit den sich konstant ändernden Marktpräferenzen Schritt halten zu können. Die vorliegende Arbeit hat zum Ziel ein Rahmenwerk vorzulegen, welches es erlaubt, die Kaffeequalität aus heterogenen Hanglagen einschätzen, kontrollieren und beeinflussen zu können. Im ersten Teil der Dissertation werden räumliche Vorhersagemodelle entwickelt und getestet, um den komparativen Vorteil von Umweltnischen zu bestimmen. Im zweiten Teil erfolgt die Analyse der systematischen Anbaupraktiken, um den komparativen Standortvorteil der Bauern auch kompetitiv nutzen zu können. Kommerzielle sensorische Daten von Kaffees aus den kolumbianischen Departamentos (entspricht Bundesländern in Deutschland) Cauca und Antioquia, aus dem Departamento Veracruz in Mexiko, und aus den fünf Kaffeebauzonen in Honduras wurden verwendet, um das Rahmenwerk zu entwickeln und zu testen. Die Ergebnisse zeigen, dass das Rahmenwerk höchst brauchbar und die mit dem Rahmenwerk generierte Information höchst neuartig, hoch bis mittelmässig umsetzbar, und mittelmässig zugänglich ist. Insgesamt lassen sich folgende Schlussfolgerungen ziehen: (1) Das Produktionsumfeld (natürliche Umwelt, agronomisches Umfeld und Nachernteverfahren) ist standortsvariable. (2) Die Tassenqualität hängt vom Produktionsumfeld ab. Die Kombination der qualitätsbeeinflussenden Faktoren variiert von Standort zu Standort und ebenfalls der Beitrag der einzelnen Faktoren. (3) Limitierende Produktionsfaktoren konnten identifiziert und deren Einfluss quantifiziert werden. Dies erlaubt eine systematische Kontrolle und Beeinflussung einzelner Faktoren, um die Produktqualität verbessern zu können. (4) Ortsspezifische, systematische und zyklische Qualitätskontrollprozesse sind notwendig, um die Variabilität der Produktqualität zu verringern und ein vom Markt nachgefragtes Produkt herzustellen zu können. (5) Die Herangehensweise beinhaltet zwei Teilschritte. Zuerst werden geeignete Nischen identifiziert und darauf basierend das ortspezifische Qualitätsmanagement definiert. (6) Managementinterventionen sind nicht immer statistisch signifikant, aber trotzdem oft relevant für den Bauern. (7) Qualitative Methoden zur Qualitätskontrolle, basierend auf kommerziellen Daten, sind brauchbare Indikatoren für die Erfassung der Tassenqualität, so lange gut ausgebildete Verkoster in Voruntersuchungen ausgewählt wurden

Natural and Technological Hazards in Urban Areas

Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events