Comparing and modeling land use organization in cities

The advent of geolocated ICT technologies opens the possibility of exploring how people use space in cities, bringing an important new tool for urban scientists and planners, especially for regions where data is scarce or not available. Here we apply a functional network approach to determine land use patterns from mobile phone records. The versatility of the method allows us to run a systematic comparison between Spanish cities of various sizes. The method detects four major land use types that correspond to different temporal patterns. The proportion of these types, their spatial organization and scaling show a strong similarity between all cities that breaks down at a very local scale, where land use mixing is specific to each urban area. Finally, we introduce a model inspired by Schelling's segregation, able to explain and reproduce these results with simple interaction rules between different land uses.Comment: 9 pages, 6 figures + Supplementary informatio

Determinants of a digital divide in Sub-Saharan Africa : a spatial econometric analysis of cell phone coverage

Most discussions of the digital divide treat it as a"North-South"issue, but the conventional dichotomy doesn't applyto cell phones in Sub-Saharan Africa. Although almost all Sub-Saharan countries are poor by international standards, they exhibit great disparities in coverage by cell telephone systems. Buys, Dasgupta, Thomas and Wheeler investigate the determinants of these disparities with a spatially-disaggregated model that employs locational information for cell-phone towers across over 990,000 4.6-km grid squares in Sub-Saharan Africa. Using probit techniques, a probability model with adjustments for spatial autocorrelation has been estimated that relates the likelihood of cell-tower location within a grid square to potential market size (proximate population); installation and maintenance cost factors related to accessibility (elevation, slope, distance from a main road, distance from the nearest large city); and national competition policy. Probit estimates indicate strong, significant results for the supply-demand variables, and very strong results for the competition policy index. Simulations based on the econometric results suggest that a generalized improvement in competition policy to a level that currently characterizes the best-performing states in Sub-Saharan Africa could lead to huge improvements in cell-phone area coverage for many states currently with poor policy performance, and an overall coverage increase of nearly 100 percent.E-Business,ICT Policy and Strategies,Population Policies,Technology Industry,Geographical Information Systems

CellTradeMap: Delineating trade areas for urban commercial districts with cellular networks

Understanding customer mobility patterns to com-mercial districts is crucial for urban planning, facility manage-ment, and business strategies. Trade areas are a widely appliedmeasure to quantify where the visitors are from. Traditionaltrade area analysis is limited to small-scale or store-level studiesbecause information such as visits to competitor commercialentities and place of residence is collected by labour-intensivequestionnaires or heavily biased location-based social media data.In this paper, we propose CellTradeMap, a novel district-leveltrade area analysis framework using mobile flow records (MFRs),a type of fine-grained cellular network data. CellTradeMap ex-tracts robust location information from the irregularly sampled,noisy MFRs, adapts the generic trade area analysis frameworkto incorporate cellular data, and enhances the original trade areamodel with cellular-based features. We evaluate CellTradeMap ona large-scale cellular network dataset covering 3.5 million mobilephone users in a metropolis in China. Experimental results showthat the trade areas extracted by CellTradeMap are aligned withdomain knowledge and CellTradeMap can model trade areaswith a high predictive accuracy

Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy

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Understanding Human Mobility with Emerging Data Sources: Validation, spatiotemporal patterns, and transport modal disparity

Human mobility refers to the geographic displacement of human beings, seen as individuals or groups, in space and time. The understanding of mobility has broad relevance, e.g., how fast epidemics spread globally. After 2030, transport is likely to become the sector with the highest emissions in the 2\ub0C\ua0scenario. Better informed policy-making requires up-to-date empirical mobility data with good quality. However, the conventional methods are limited when dealing with new challenges. The prevalence of digital technologies enables a large-scale collection of human mobility traces, through social media data and GPS-enabled devices etc, which contribute significantly to the understanding of human mobility. However, their potentials for the further application are not fully exploited.This thesis uses emerging data sources, particularly Twitter data, to enhance the understanding of mobility and apply the obtained knowledge in the field of transport. The thesis answers three questions: Is Twitter a feasible data source to represent individual and population mobility? How are Twitter data used to reveal the spatiotemporal dynamics of mobility? How do Twitter data contribute to depicting the modal disparity of travel time by car vs public transit? In answering these questions, the methodological contribution of this thesis lies in the applied side of data science.Using geotagged Twitter data, mobility is firstly described by abstract metrics and physical models; in Paper A to reveal the population heterogeneity of mobility patterns using data mining techniques; and in Paper B to estimate travel demand with a novel approach to address the sparsity issue of Twitter data. In Paper C, GIS techniques are applied to combine the travel demand as revealed by Twitter data and the transportation network to give a more realistic picture of the modal disparity in travel time between car and public transit in four cities in different countries at a high spatial and temporal granularity. The validation of using Twitter data in mobility study contributes to better utilisation of this low-cost mobility data source. Compared with a static picture obtained by conventional data sources, the dynamics introduced by social media data among others contribute to better-informed policymaking and transport planning