960 research outputs found

    A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

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    Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution

    The impact of the spatial superimposition of point based statistical configurations on assessing spatial autocorrelation

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    Ambient user-generated geo-information like that from geosocial media is collected using liberal, unmoderated acquisition modes. This offers a high degree of freedom regarding content. However, the collected information is influenced by idiosyncratic spatial perceptions. The resulting datasets are thus heterogeneous and comprise different (often inseparable), spatially and temporally superimposed statistical populations. Traditional notions of stationarity, which are oftentimes required in spatial analysis, are therefore frequently violated and conclusions about disclosed spatial structures might be misleading. This paper examines how the spatial superimposition of statistical populations influences the spatial autocorrelation estimator Moran‟s I. The approach chosen allows to gain insights beyond specific empirical datasets and with full flexibility in parameterization. A synthetic point pattern is therefore constructed, which contains two overlapping, differently scaled sub-patterns. Normally distributed values drawn from populations with different means and variances are repeatedly assigned to these, and Moran‟s I is calculated for 20,000 overall configurations. Each parameter value thereby corresponds to a multiple of the same parameter value of the other population. The results show strong influences of discrepancies in statistical parameter values of co-located populations on the characterization of spatial patterns. While differences in mean values change the magnitude of Moran‟s I, whereas differences in variances increase the range of the measure. The scale associated with the dominant of the involved populations further influences the magnitude of Moran‟s I. These results suggest that the spatial analysis of ambient user-generated geo-information from unmoderated acquisition modes may require the consideration of different superimposed statistical populations to ensure meaningful resul

    Characterising and Modelling Urban Freight in Developing Economies

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    Urban freight systems in developing countries present significant challenges due to their complexity. Authorities often have inadequate institutional structures, making it difficult to identify and implement relevant initiatives. This thesis aims to characterise the systems in developing economies and model freight demand using innovative approaches by considering new attributes, dimensions and alternatives. As a first modelling step, freight (trip) generation was improved by considering spatial and locational determinants, as freight activities are strongly related to spatial and locational characteristics of establishments. Spatial models were developed using a combined spatial autoregressive model (SAR) and geographically weighted regression (GWR) or multiscale GWR (MGWR) (GWR/MGWR-SAR model). This model accounted for non-linearity, spatial heterogeneity and spatial dependency and demonstrated significant improvements (R2 0.29-0.71, RMSE reduced by 71% and AIC value by 56%). Shipment size decisions related to the choice of truck type were strongly timedependent, with commodity type, activities at the trip end, truck body type and industry sector affecting the preferences. Freight demand, including shipment size choices, was influenced by economic fluctuations, with shipment size declining after an economic slowdown. In freight demand modelling, it is imperative to consider economic conditions, especially those in developing countries, which are often susceptible to strong economic fluctuations. The models were applied in ex ante testing of a policy restricting large trucks from entering a city centre, as commonly considered in many developing countries. In tests, the truck restriction was accompanied by single-tier and two-tier distribution systems. The results showed that the two-tier system had a slight advantage over the single-tier system regarding operational expenditure and emission levels. Truck restriction was generally counterproductive, even when accompanied by distribution systems with greater speed and efficiency. We conclude that the models enhance the accurate prediction of freight demand patterns. The ex ante evaluation of policy alternatives supports the decision-making process for urban freight systems of large cities in developing economies. The models allow considering relevant practical, local contextual conditions

    Analyzing spatial patterns and dynamics of landscapes and ecosystem services – Exploring fine-scale data and indicators

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    In den vergangenen Jahrzehnten hat der Einfluss des Menschen auf Ökosysteme stark zugenommen. Tendenzen der LandnutzungsĂ€nderung, darunter die Ausdehnung von StĂ€dten und die Intensivierung der Landwirtschaft als Folge des Bevölkerungsanstiegs und damit des Nahrungsmittel- und Energiebedarfs, fĂŒhren zu Umweltproblemen wie dem Verlust von Lebensraum und biologischer Vielfalt. Die zunehmende VerfĂŒgbarkeit von Daten mit feiner rĂ€umlicher Auflösung kann die Analyse von Merkmalen und Prozessen in Landschaften mit Hilfe von rĂ€umlichen Metriken unterstĂŒtzen. Das Ziel dieser Arbeit ist es, feinskalige Daten und rĂ€umliche Metriken zu integrieren, um Indikatoren zur Messung und Bewertung von Landnutzung, Ökosystemdienstleistungen und deren rĂ€umlichen Mustern zu entwickeln und folgende Fragen zu beantworten: Wie können LandnutzungsĂ€nderungen und Ökosystemleistungen einer Landschaft beschrieben und analysiert werden? Und, wie kann die Landschaftsperspektive zu unserem VerstĂ€ndnis von Landsystemen beitragen? In zwei verschiedenen Weltregionen werden Landschaften mit Hilfe von Hexagonen als rĂ€umliche Einheiten untersucht. Diese dienen zur Analyse von rĂ€umlichen Mustern und Beziehungen zwischen verschiedenen Indikatoren (z. B. Ökosystemdienstleistungen) und die Konzeptualisierung von Prozessen auf Landschaftsebene. Obwohl sich einige PhĂ€nomene auf feinen rĂ€umlichen Skalen manifestieren, ist es fĂŒr die Operationalisierung und Überwachung dieser Prozesse notwendig, ‚herauszuzoomen‘. Der Landschaftsansatz im Zusammenhang mit Ökosystemleistungen bietet wichtige Perspektiven im Hinblick auf Umweltauswirkungen, die durch LandnutzungsĂ€nderungen verursacht werden. Dabei können Indikatoren, die die ökologische, ökonomische und soziale Dimension verknĂŒpfen, dazu beitragen, regionalspezifisches Wissen ĂŒber Landschaftsdynamiken zu erlangen und dieses Wissen an EntscheidungstrĂ€ger weiterzugeben, um gezielte Maßnahmen fĂŒr ein nachhaltiges Landmanagement zu entwickeln.Over the last decades, anthropogenic pressures on ecosystems have been increasing. Trends of land use change including urban expansion and agricultural intensification driven by population increase, and hence food and energy demand, cause environmental challenges including habitat and biodiversity loss. Analyzing major trends of land use change requires additional metrics to capture local processes on a landscape spatial scale. Increasing fine-scale data availability can support analyses of characteristics and processes of landscapes with the help of spatial metrics, e.g. distance or density measures. The aims of this thesis are to incorporate fine-scale data and spatial metrics to develop indicators to measure and assess land-use, ecosystem services (ESS) and their spatial patterns to answer the following questions: How can land use change and ecosystem services of landscapes be described and analyzed? And how can the landscape perspective contribute to our understanding of land systems? The thesis includes three case studies in two different world regions: 1) characteristics of land use within a peri-urban gradient in Dar es Salaam, Tanzania, 2) characteristics of agricultural landscapes in Brandenburg, Germany, and 3) ecosystem service relationships at different spatial units and scales. In both regions, landscapes are investigated with hexagons as spatial units for the analysis of spatial patterns and relationships among different indicators (i.e., ESS) and conceptualize processes on a landscape level. The landscape approach in context with ecosystem services offers important perspectives regarding environmental impacts caused by land use change. Thereby, metrics integrating the ecological, economic, and social dimensions can support obtaining region-specific knowledge on landscape dynamics and transferring this knowledge to decision-makers to design targeted measures towards sustainable land management

    Mapping the transcriptome: Realizing the full potential of spatial data analysis

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    RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets

    Doctor of Philosophy

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    dissertationThis research focuses on the application of geographic information systems (GIS) and spatial analysis methods to urban and regional development studies. GIS-based spatial modeling approaches have recently been used in examining regional development disparities and urban growth. Through the cases of Guangdong province and the city of Dongguan, the study employs a spatial-temporal, multiscale, and multimethodology approach in analyzing geographically referenced socioeconomic and remote sensing data. A general spatial data analysis framework is set through a study of regional development in China's Guangdong province and urban growth in the city of Dongguan. Three intensive spatial statistical analyses are carried out. First, the dissertation investigates the spatial dynamics of regional inequality through Markov chains and spatial Markov-chain analyses. In so doing, it addresses the effect of self-reinforcing agglomeration on regional disparities. Multilevel modeling is further employed to evaluate the relative importance of regional development mechanisms in Guangdong. Second, a spatial filtering perspective is employed for understanding the spatial effects on multiscalar characteristics of regional inequality in Guangdong. Spatial panel and space-time regression models are integrated to detail the spatial and temporal heterogeneity of underlying mechanisms behind regional inequality. Third, drawing upon a set of high-quality remote sensing data in the city of Dongguan, the dissertation analyzes the spatial-temporal dynamics and spatial determinants of urban growth in a rapid industrializing area. Through the application of landscape metrics, three types of urban growth, including infill, spontaneous, and edge expansion, are distinguished, addressing the diverse spatial patterns at different stages of urban growth. A spatial logistic approach is further developed to model the spatial variations of urban growth determinants within the Dongguan city. In short, the dissertation finds that regional inequality in the Guangdong province is sensitive to spatial scales, dependence, and the core-periphery structure therein. The evolution of inequality can hardly be simplified into either convergence or divergence trajectories. Furthermore, development mechanisms and urban growth determinants are apparently different in space and are sensitive to spatial hierarchies and regimes. Overall, through the application of GIS spatial modeling techniques, the dissertation has provided more valuable information about spatial effects on China's urban and regional development under economic transition and highlights the importance of taking into consideration spatial dimensions in urban and regional development studies

    Towards the statistical analysis and visualization of places (Short Paper)

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    The concept of place recently gains momentum in GIScience. In some fields like human geography, spatial cognition or information theory, this topic already has a longer scholarly tradition. This is however not yet completely the case with statistical spatial analysis and cartography. Despite that, taking full advantage of the plethora of user-generated information that we have available these days requires mature place-based statistical and visualization concepts. This paper contributes to these developments: We integrate existing place definitions into an understanding of places as a system of interlinked, constituent characteristics. Based on this, challenges and first promising conceptual ideas are discussed from statistical and visualization viewpoints

    Analysing and predicting micro-location patterns of software firms

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    While the effects of non-geographic aggregation on inference are well studied in economics, research on geographic aggregation is rather scarce. This knowledge gap together with the use of aggregated spatial units in previous firm location studies result in a lack of understanding of firm location determinants at the microgeographic level. Suitable data for microgeographic location analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI), especially the OpenStreetMap (OSM) project, and the increasing availability of official (open) geodata. In this paper, we use a comprehensive dataset of three million street-level geocoded firm observations to explore the location pattern of software firms in an Exploratory Spatial Data Analysis (ESDA). Based on the ESDA results, we develop a software firm location prediction model using Poisson regression and OSM data. Our findings demonstrate that the model yields plausible predictions and OSM data is suitable for microgeographic location analysis. Our results also show that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analysed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations
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