Marco Helbich

Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion

This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades.

Toward mapping land-use patterns from volunteered geographic information

A large number of applications have been launched to gather geo-located information from the public. This article introduces an approach toward generating land-use patterns from volunteered geographic information (VGI) without applying remote-sensing techniques and/or engaging official data. Hence, collaboratively collected OpenStreetMap (OSM) data sets are employed to map land-use patterns in Vienna, Austria. Initially the spatial pattern of the landscape was delineated and thereafter the most relevant land type was assigned to each land parcel through a hierarchical GIS-based decision tree approach. To evaluate the proposed approach, the results are compared with the Global Monitoring for Environment and Security Urban Atlas (GMESUA) data. The results are compared in two ways: first, the texture of the resulting land-use patterns is analyzed using texture-variability analysis. Second, the attributes assigned to each land segment are evaluated. The achieved land-use map shows kappa indices of 91, 79, and 76% agreement for location in comparison with the GMESUA data set at three levels of classification. Furthermore, the attributes of the two data sets match at 81, 67, and 65%. The results demonstrate that this approach opens a promising avenue to integrate freely available VGI to map land-use patterns for environmental planning purposes.

Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R 2) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R 2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.

Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria

Modelling spatial heterogeneity (SH) is a controversial subject in real estate economics. Single-family-home prices in Austria are explored to investigate the capability of global and locally weighted hedonic models. Even if regional indicators are not fully capable to model SH and technical amendments are required to account for unmodelled SH, the results emphasise their importance to achieve a well-specified model. Due to SH beyond the level of regional indicators, locally weighted regressions are proposed. Mixed geographically weighted regression (MGWR) prevents the limitations of fixed effects by exploring spatially stationary and non-stationary price effects. Besides reducing prediction errors, it is concluded that global model misspecifications arise from improper selected fixed effects. Reported findings provide evidence that the SH of implicit prices is more complex than can be modelled by regional indicators or purely local models. The existence of both stationary and non-stationary effects implies that the Austrian housing market is economically connected.

Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai

Abstract The rapid growth of megacities requires special attention among urban planners worldwide, and particularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this will bring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use change models – and in particular non-spatial logistic regression models (LR) and auto-logistic regression models (ALR) – for the Mumbai region over the period 1973–2010, in order to determine the drivers behind spatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variations in driving-forces across space. The study comes to two main conclusions. First, both global models suggest similar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimated coefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporal and spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth processes, and so can assist context-related planning and policymaking activities when seeking to secure a sustainable urban future.

Geospatial examination of lithium in drinking water and suicide mortality

BackgroundLithium as a substance occurring naturally in food and drinking water may exert positive effects on mental health. In therapeutic doses, which are more than 100 times higher than natural daily intakes, lithium has been proven to be a mood-stabilizer and suicide preventive. This study examined whether natural lithium content in drinking water is regionally associated with lower suicide rates.MethodsPrevious statistical approaches were challenged by global and local spatial regression models taking spatial autocorrelation as well as non-stationarity into account. A Geographically Weighted Regression model was applied with significant independent variables as indicated by a spatial autoregressive model.ResultsThe association between lithium levels in drinking water and suicide mortality can be confirmed by the global spatial regression model. In addition, the local spatial regression model showed that the association was mainly driven by the eastern parts of Austria.ConclusionsAccording to old anecdotic reports the results of this study support the hypothesis of positive effects of natural lithium intake on mental health. Both, the new methodological approach and the results relevant for health may open new avenues in the collaboration between Geographic Information Science, medicine, and even criminology, such as exploring the spatial association between violent or impulsive crime and lithium content in drinking water.

The Impact of Hurricanes on Crime: A Spatio-Temporal Analysis in the City of Houston, Texas

The impact that natural disasters have on crime is not well understood. In general, it is assumed that crime declines shortly after the disaster and slowly increases to pre-disaster levels over time. However, this assumption is not always confirmed by the few empirical studies that have been conducted to date. In this paper we analyze the impacts that Hurricane Rita, and for the purpose of comparison, Hurricane Katrina had on the temporal and spatial distributions of reported crimes in the city of Houston, TX. Crime data were collected before, during, and after the landfall of both hurricanes. The modeling part of this paper focused on primarily spatio-temporal and local regression models at the local scale. Spatio-temporal models were applied to identify potential spatio-temporal crime clusters associated with the hurricanes. A local regression model in the form of a geographically weighted regression was applied to explore relationships between crime clusters and possible underlying factors leading to the creation of said clusters. The results show that while Hurricane Katrina did not have any apparent impact on crime, Hurricane Rita led to a significant short-term increase in burglaries and auto thefts. The post important result was the identification of a large, highly significant spatio-temporal burglary cluster located in the northeastern part of Houston. This cluster lasted from a few days before to a few days after the landfall of Hurricane Rita. Empirical evidence was found that the mandatory evacuation order that was issued prior to the arrival of Hurricane Rita led to a short-time spike in burglaries. It was assumed that these crimes were committed by individuals who did not follow the evacuation order, but instead burglarized the residences of individuals who did evacuate. No mandatory evacuation order was issued for Hurricane Katrina. Altogether, three variables including the percentage of African Americans, the percentage of persons living below the poverty level, and the distance to the nearest police station was identified as having a positive relationship with the increase in burglaries associated with Hurricane Rita

Data-Driven Regionalization of Housing Markets

This article presents a data-driven framework for housing market segmentation. Local marginal house price surfaces are investigated by means of mixed geographically weighted regression and are reduced to a set of principal component maps, which in turn serve as input for spatial regionalization. The out-of-sample prediction error of a hedonic pricing model is applied to determine a “near-optimal” number of spatially coherent and homogeneous submarkets. The usefulness of this method is demonstrated with a detailed data set for the Austrian housing market. The results provide evidence that submarkets must always be considered, however they are defined, and that the proposed submarket taxonomy on a regional level significantly improves predictive quality compared to (1) a traditional pooled model, (2) a model that uses an ad hoc submarket definition based on administrative units, and (3) a model incorporating an alternative submarket definition on the basis of aspatial k-means clustering. Moreover, it is concluded that the Austrian housing market is characterized by regional determinants and that geography is the most important component determining the house prices.

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.

Postsuburban Spatial Evolution of Vienna's Urban Fringe: Evidence from Point Process Modeling

Metropolitan areas today are faced with pervasive changes of their urban spatial structure and are reshaped by postsuburbanization processes. In this study, one example of such postsuburban restructuring, the multinucleated monofunctional clustering of higher-order services, is investigated in the urban fringe of Vienna, Austria. The methodological framework uses microgeographic data for 2006 and applies a case-control point process modeling approach, which accounts for nonstationarity in first-order effects. The results show a relocation of highly specialized firms into the outer metropolitan ring, where these firms provide functional enrichment. This disagrees with the classical notion of a central place hierarchy. The urban fringe thus increasingly conforms to the core city. This spatial functional agglomeration shows statistically significant evidence of a bicentric urban structure, with the two new subcenters located in traditional suburban areas. Accordingly, Viennas urban fringe is being altered by new postsuburban forms.