Placing Wikimapia: an exploratory analysis

Wikimapia is a major privately-owned volunteered geographic information (VGI) project to collect information about places. Over the past ten years, Wikimapia has attracted hundreds of thousands of contributors and collected millions of data points, including towns, restaurants, lakes, and tourist attractions (http://wikimapia.org). Unlike OpenStreetMap, Wikimapia adopts a "placial" perspective, favouring rich descriptions over detailed geometries and encouraging the collection of textual and visual content about places with approximate footprints. In this article, we first trace the origin and development of Wikimapia as a for-profit project, intimately linked with search engine advertising. Drawing on an in-depth interview with a former developer, we analyse project's data model and characteristics of its community. As Wikimapia discussions are rife with copyright issues, we discuss the project's intellectual property, as well as its strategies for quality management. Second, we focus on the popularity of the project, which is crucial to the longevity and sustainability of VGI projects. Using behavioural data from Google Trends, we trace a geography of interest in Wikimapia, comparing with that in OpenStreetMap, from a temporal and spatial perspective. While OpenStreetMap attracts more interest in high-income countries, Wikimapia emerges as relatively more popular in low- and middle-income countries, countering the received notion of VGI as a Global North phenomenon. Our study suggests that Wikimapia’s popularity is steadily declining

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Global land cover (LC) maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection, and environmental planning. Due to the importance of LC, there is a pressing need to increase the temporal and spatial resolution of global LC maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, that is, CORINE, Urban Atlas (UA), OpenStreetMap, and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large disagreements for certain classes are evident, in particular wetlands. However, overall, GlobeLand30 is shown to be a useful product for characterizing LC in Germany, and paves the way for further regional and national validation efforts

Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis

Spatial and spatiotemporal analyses are exceedingly relevant to determine criminogenic factors. The estimation of Poisson and negative binomial models (NBM) is complicated by spatial autocorrelation. Therefore, first, eigenvector spatial filtering (ESF) is introduced as a method for spatiotemporal mapping to uncover time-invariant crime patterns. Second, it is demonstrated how ESF is effectively used in criminology to invalidate model misspecification, i.e., residual spatial autocorrelation, using a nonviolent crime dataset for the metropolitan area of Houston, Texas, over the period 2005– 2010. The results suggest that local and regional geography significantly contributes to the explanation of crime patterns. Furthermore, common space-time eigenvectors selected on an annual basis indicate striking spatiotemporal patterns persisting over time. The findings about the driving forces behind Houston’s crime show that linear and nonlinear, spatially filtered, NBMs successfully absorb latent autocorrelation and, therefore, prevent parameter estimation bias. The consideration of a spatial filter also increases the explanatory power of the regressions. It is concluded that ESF can be highly recommended for the integration in spatial and spatiotemporal modeling toolboxes of law enforcement agencies

Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis

Spatial and spatiotemporal analyses are exceedingly relevant to determine criminogenic factors. The estimation of Poisson and negative binomial models (NBM) is complicated by spatial autocorrelation. Therefore, first, eigenvector spatial filtering (ESF) is introduced as a method for spatiotemporal mapping to uncover time-invariant crime patterns. Second, it is demonstrated how ESF is effectively used in criminology to invalidate model misspecification, i.e., residual spatial autocorrelation, using a nonviolent crime dataset for the metropolitan area of Houston, Texas, over the period 2005– 2010. The results suggest that local and regional geography significantly contributes to the explanation of crime patterns. Furthermore, common space-time eigenvectors selected on an annual basis indicate striking spatiotemporal patterns persisting over time. The findings about the driving forces behind Houston’s crime show that linear and nonlinear, spatially filtered, NBMs successfully absorb latent autocorrelation and, therefore, prevent parameter estimation bias. The consideration of a spatial filter also increases the explanatory power of the regressions. It is concluded that ESF can be highly recommended for the integration in spatial and spatiotemporal modeling toolboxes of law enforcement agencies

Computational approaches for urban environments

This book aims to promote the synergistic usage of advanced computational methodologies in close relationship to geospatial information across cities of different scales. A rich collection of chapters subsumes current research frontiers originating from disciplines such as geography, urban planning, computer science, statistics, geographic information science, and remote sensing. The topics covered in the book are of interest to researchers, postgraduates, practitioners, and professionals. The editors hope that the scientific outcome of this book will stimulate future urban-related international and interdisciplinary research, bringing us closer to the vision of a “new science of citie

Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran

Rapid urban growth is becoming a serious problem in most developing countries. Tehran, the capital of Iran, stands out as a vibrant metropolitan area, facing uncontrolled urban expansion. Public authorities and decision makers require planning criteria regarding possible spatial developments. To monitor past developmental trends and to simulate emerging spatiotemporal patterns of urban growth, this research applies a geosimulation approach that couples agent-based modeling with multicriteria analysis (MCA) for the period between 1986 and 2006. To model the major determinants controlling urban development, three agent groups are defined, namely developer agents, government agents, and resident agents. The behaviors of each agent group are identified by qualitative surveys and are considered separately using multi-criteria analysis. The interactions of the agents are then combined through overlay functions within a Geographic Information System (GIS). This analysis results in the creation of a propensity surface of growth that is able to identify the most probable sites for urban development. Subsequently, a Markov Chain Model (MCM) and a concise statistical extrapolation are used to determine the amount of probable future expansion in Tehran. For validation purposes, the model is estimated using 2011 data and then validated based on actual urban expansion. Given the accurate predictions of the Markov Chain Model, further predictions were carried out for 2016 and 2026. This simulation provides strong evidence that during the next decade planning authorities will have to cope with continuous as well as heterogeneously distributed urban growth. Both the monitoring of growth and simulation revealed significant developments in the northwestern part of Tehran, continuing toward the south along the interchange networks

A Morphological Approach to Predicting Urban Expansion

Many methods for modeling urban expansion are available. Most of these computational models demand a variety of large-scale environmental and socio-economic data to investigate the relationship between urban expansion and its driving forces. These requirements are not always fulfilled, particularly in developing countries due to a lack of data availability. This necessitates methods not suffering from data limitations to ease their application. Consequently, this research presents a morphological approach for predicting urban expansion on the basis of spatiotemporal dynamics of urban margins by investigating the interior metropolitan area of Tehran, Iran as a case study. To assess the model's performance, urban expansion is monitored from 1976 to 2012. The proposed model is evaluated to ensure that the prediction performance for the year 2012 is acceptable. For the year 2024, the model predicts Tehran's urban expansion at an overall R2 of 88%. Accordingly, it is concluded that: (1) although this approach only inputs urban margins, it represents a suitable and easy-to-use urban expansion model; and (2) urban planners are faced with continuing urban expansion

A Morphological Approach to Predicting Urban Expansion

Many methods for modeling urban expansion are available. Most of these computational models demand a variety of large-scale environmental and socio-economic data to investigate the relationship between urban expansion and its driving forces. These requirements are not always fulfilled, particularly in developing countries due to a lack of data availability. This necessitates methods not suffering from data limitations to ease their application. Consequently, this research presents a morphological approach for predicting urban expansion on the basis of spatiotemporal dynamics of urban margins by investigating the interior metropolitan area of Tehran, Iran as a case study. To assess the model's performance, urban expansion is monitored from 1976 to 2012. The proposed model is evaluated to ensure that the prediction performance for the year 2012 is acceptable. For the year 2024, the model predicts Tehran's urban expansion at an overall R2 of 88%. Accordingly, it is concluded that: (1) although this approach only inputs urban margins, it represents a suitable and easy-to-use urban expansion model; and (2) urban planners are faced with continuing urban expansion

The emergence and evolution of OpenStreetMap: A cellular automata approach

Collaborative mapping projects, such as OpenStreetMap (OSM), have received tremendous amounts of contributed data from voluntary participants over time. So far, most research efforts deal with data quality issues, but the OSM evolution across space and over time has not been noted. Therefore, this study is dedicated to the evolution of the contributed information in order to understand an emergent phenomenon of so-called collaborative contributing. The main objective of this paper is to monitor the evolutional pattern of OSM and predict potential future states through a cellular automata (CA) model. This is exceedingly relevant for numerous OSM-based applications. Descriptive spatiotemporal analysis of the contributions for the time period 2007–2012, using the city of Heidelberg (Germany) as a case study, reveals that early contributions are given three years after the launching of OSM, while after nearly six years, most of the areas are discovered. The simulation results for the validated CA model, predicting OSM states for 2014, provide clear evidence that most of the areas have been explored three years after people began mapping until 2010, and thereafter, the densification process has begun and will cover most parts of the city although the amount of contribution depends on the land use types