A growing body of empirical findings suggests that more satisfactory, compact, and traversable built environments can positively influence active travel, physical activity, and the walking experience. To this end, planning for better and more walkable places has been identified as a hot topic in urban studies and public health research, since. However, European-level indicators assessing aspects of pedestrian-friendly urban environments are largely lacking. This article introduces spatial and tabular data files of 17 pre-processed and microscale walkability indicators. The dataset presents relevant to the pedestrian environment information for 59 central urban areas from 26 European countries and aims to support policy analysis and assessment related to healthy and low-carbon transportation systems as well as sustainable communities. Methodologically, we applied a virtual (i.e., Google Street View) street audit tool, block-by-block and on both sides of each street and crossing segment separately. To this end, we digitized in polyline features observations and evaluations for a total of 112.577 street- and/or crossing-segments. The data collection process was a demanding and challenging process, which lasted for 21 months and involved 46 trained observers. The data tables in this paper present processed data of each audited item topic as a total share of street segments or crossings length by city. More specifically, the data tables contain indicators that describe the following seventeen themes: percent of segments with predominant commercial or/and entertainment buildings (active uses), percent of segments with access to park/plaza, percent of segments with transit stop(s), percent of segments with available public seats, percent of segments according to their street lighting conditions, percent of segments with well-maintained buildings, percent of segments where graffiti is not present, percent of segments where a bike lane is present, percent of segments where a sidewalk is present, percent of segments with well-maintained sidewalks, percent of segments with sidewalk buffers, percent of segments according to shading levels, percent of segments with wider sidewalks, percent of segments according to the number of road traffic lanes, percent of crossings with a pedestrian walk signal, percent of crossings with curb(s) ramp and percent of crossings with a marked pedestrian crosswalk. Additionally, a dedicated web-GIS platform has been designed and developed to visualize and disseminate collected data in openly available density maps of high spatial resolution (50 m × 50 m). The above data can be utilized to both raise awareness of unsatisfactory pedestrian environments and appoint them as a key health and environmental issue, as well as to assist European policy-makers to apply urban mobility strategies and monitor progress in urban sustainability and public health goals.
A number of urban growth models have been developed to simulate and predict urban expansion. Most of these models have common objectives; however, they differ in terms of calibration and execution methodologies. GIS spatial computations and data processing capabilities have given us the ability to draw more effective simulation results for increasingly complex scenarios. In this paper, we apply and evaluate a methodology to create a hybrid cellular-automaton- (CA) and agent-based model (ABM) using raster and vector data from the Urban Atlas project as well as other open data sources. We also present and evaluate three different methods to calibrate and evaluate the model. The model has been applied and evaluated by a case study on the city of Athens, Greece. However, it has been designed and developed with the aim of being applicable to any city available in the Urban Atlas project.
Fear of crime is a social phenomenon that mainly affects the population of urban communities and it is recognized as an issue by both the academic community and society itself. To study the phenomenon, it is necessary to collect primary data, either using traditional data collection methods or using well-established online questionnaires. This paper describes the process and architecture of developing an interactive data survey, analysis, and geovisualization web-based platform to support online questionnaires and surveys, related to the urban fear of crime. The main goal is to provide tools and utilities for researchers, journalists, groups or individuals, interested in the scientific aspect of fear of crime, to collect related data and analyze them within a common interface. The fear of crime platform utilizes a client-server Web-GIS application that gives access to a worldwide spatial database. As the fear of crime platform is a dynamic ecosystem that grows up every day, this database is also growing proportionally by individuals around the world. The project’s development is accessible at the following web address: www.fearofcrime.com
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