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Geographical Analysis

Impact factor: 1.5 5-Year impact factor: 1.814 Print ISSN: 0016-7363 Online ISSN: 1538-4632 Publisher: Wiley Blackwell (Blackwell Publishing)

Subject: Geography

Most recent papers:

  • Bayesian Spatial Filtering for Hedonic Models: An Application for the Real Estate Market.
    Pilar Gargallo, Jesús Angel Miguel, Manuel Juan Salvador.
    Geographical Analysis. October 04, 2017
    This article presents a Bayesian method based on spatial filtering to estimate hedonic models for dwelling prices with geographically varying coefficients. A Bayesian Adaptive Sampling algorithm for variable selection is used, which makes it possible to select the most appropriate filters for each hedonic coefficient. This approach explores the model space more systematically and takes into account the uncertainty associated with model estimation and selection processes. The methodology is illustrated with an application for the real estate market in the Spanish city of Zaragoza and with simulated data. In addition, an exhaustive comparison study with a set of alternatives strategies used in the literature is carried out. Our results show that the proposed Bayesian procedures are competitive in terms of prediction; more accurate results are obtained in the estimation of the regression coefficients of the model, and the multicollinearity problems associated with the estimation of the regression coefficients are solved.
    October 04, 2017   doi: 10.1111/gean.12136   open full text
  • Privacy and False Identification Risk in Geomasking Techniques.
    Dara E. Seidl, Piotr Jankowski, Keith C. Clarke.
    Geographical Analysis. October 03, 2017
    Recent years have seen an increase in location privacy research, including the application of geomasking procedures. Geographical masks aim to protect privacy and preserve spatial information through the displacement of point data. False identification, or the mistaken association of data with the incorrect person or household, is an unexplored issue in geomasking, despite legal protections against false association. This study introduces a topological framework for assessing identification risk and examines the risk of false identification in four masking techniques: random perturbation, donut masking, and the newer Voronoi and MGRS masking techniques. While Voronoi masking is found to best preserve the clustering properties of a sample of urban foreclosure data, the other three masking techniques result in better protection against correct and false identification.
    October 03, 2017   doi: 10.1111/gean.12144   open full text
  • A Topological Representation for Taking Cities as a Coherent Whole.
    Bin Jiang.
    Geographical Analysis. September 14, 2017
    A city is a whole, as are all cities in a country. Within a whole, individual cities possess different degrees of wholeness, defined by Christopher Alexander as a life‐giving order or simply a living structure. To characterize the wholeness and in particular to advocate for wholeness as an effective design principle, this article develops a geographic representation that views cities as a whole. This geographic representation is topology‐oriented, so fundamentally differs from existing geometry‐based geographic representations. With the topological representation, all cities are abstracted as individual points and put into different hierarchical levels, according to their sizes and based on head/tail breaks—a classification and visualization tool for data with a heavy tailed distribution. These points of different hierarchical levels are respectively used to create Thiessen polygons. Based on polygon–polygon relationships, we set up a complex network. In this network, small polygons point to adjacent large polygons at the same hierarchical level and contained polygons point to containing polygons across two consecutive hierarchical levels. We computed the degrees of wholeness for individual cities, and subsequently found that the degrees of wholeness possess both properties of differentiation and adaptation. To demonstrate, we developed four case studies of all China and U.K. natural cities, as well as Beijing and London natural cities, using massive amounts of street nodes and Tweet locations. The topological representation and the kind of topological analysis in general can be applied to any design or pattern, such as carpets, Baroque architecture and artifacts, and fractals in order to assess their beauty, echoing the introductory quote from Christopher Alexander.
    September 14, 2017   doi: 10.1111/gean.12145   open full text
  • Stochastic Efficiency of Bayesian Markov Chain Monte Carlo in Spatial Econometric Models: An Empirical Comparison of Exact Sampling Methods.
    Levi John Wolf, Luc Anselin, Daniel Arribas‐Bel.
    Geographical Analysis. September 06, 2017
    Spatial econometric specifications pose unique computational challenges to Bayesian analysis, making it difficult to estimate models efficiently. In the literature, the main focus has been on extending Bayesian analysis to increasingly complex spatial models. The stochastic efficiency of commonly used Markov Chain Monte Carlo (MCMC) samplers has received less attention by comparison. Specifically, Bayesian methods to analyze effective sample size and samplers that provide large effective size have not been thoroughly considered in the literature. Thus, we compare three MCMC techniques: the familiar Metropolis‐within‐Gibbs sampling, Slice‐within‐Gibbs sampling, and Hamiltonian Monte Carlo. The latter two methods, while common in other domains, are not as widely encountered in Bayesian spatial econometrics. We assess these methods across four different scenarios in which we estimate the spatial autoregressive parameter in a mixed regressive, spatial autoregressive specification (or, spatial lag model). We find that off‐the‐shelf implementations of the newer high‐yield simulation techniques require significant adaptation to be viable. We further find that the effective sizes are often significantly smaller than nominal sizes. In addition, we find that stopping simulation early may understate posterior credible interval widths when effective sample size is small. More broadly, we suggest that sample information and stopping rules deserve more attention in both applied and basic Bayesian spatial econometric research.
    September 06, 2017   doi: 10.1111/gean.12135   open full text
  • An Introduction to the Network Weight Matrix.
    Alireza Ermagun, David Levinson.
    Geographical Analysis. July 05, 2017
    This study introduces the network weight matrix as a replacement for the spatial weight matrix to measure the spatial dependence between links of a network. This matrix stems from the concepts of betweenness centrality and vulnerability in network science. The elements of the matrix are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrix has distinctive characteristics, which are capable of reflecting spatial dependence between traffic links: (1) elements are allowed to have negative and positive values capturing the competitive and complementary nature of links, (2) diagonal elements are not fixed to zero, which takes the self‐dependence of a link upon itself into consideration, and (3) elements not only reflect the spatial dependence based on the network structure, but they acknowledge the demand configuration as well. We verify the network weight matrix by modeling traffic flows in a 3 × 3 grid test network with 9 nodes and 24 directed links connecting 72 origin‐destination (OD) pairs. Models encompassing the network weight matrix outperform both models without spatial components and models with the spatial weight matrix. The network weight matrix represents a more accurate and defensible spatial dependency between traffic links, and offers the potential to augment traffic flow prediction.
    July 05, 2017   doi: 10.1111/gean.12134   open full text
  • Multi‐Type, Multi‐Zone Facility Location.
    Andries M. Heyns, Jan H. van Vuuren.
    Geographical Analysis. June 30, 2017
    The placement of facilities according to spatial and/or geographic requirements is a popular problem within the domain of location science. Objectives that are typically considered in this class of problems include dispersion, median, center, and covering objectives—and are generally defined in terms of distance or service‐related criteria. With few exceptions, the existing models in the literature for these problems only accommodate one type of facility. Furthermore, the literature on these problems does not allow for the possibility of multiple placement zones within which facilities may be placed. Due to the unique placement requirements of different facility types—such as suitable terrain that may be considered for placement and specific placement objectives for each facility type—it is expected that different suitable placement zones for each facility type, or groups of facility types, may differ. In this article, we introduce a novel mathematical treatment for multi‐type, multi‐zone facility location problems. We derive multi‐type, multi‐zone extensions to the classical integer‐linear programming formulations involving dispersion, centering and maximal covering. The complexity of these formulations leads us to follow a heuristic solution approach, for which a novel multi‐type, multi‐zone variation of the non‐dominated sorting genetic algorithm‐II algorithm is proposed and employed to solve practical examples of multi‐type, multi‐zone facility location problems.
    June 30, 2017   doi: 10.1111/gean.12131   open full text
  • A Comparison of Spatially Varying Regression Coefficient Estimates Using Geographically Weighted and Spatial‐Filter‐Based Techniques.
    Taylor M. Oshan, A. Stewart Fotheringham.
    Geographical Analysis. June 30, 2017
    Geographically weighted regression (GWR) is a technique that explores spatial nonstationarity in data‐generating processes by allowing regression coefficients to vary spatially. It is a widely applied technique across domains because it is intuitive and conforms to the well‐understood framework of regression. An alternative method to GWR that has been suggested is spatial filtering, which it has been argued provides a superior alternative to GWR by producing spatially varying regression coefficients that are not correlated with each other and which display less spatial autocorrelation. It is, therefore, worthwhile to examine these claims by comparing the output from both methods. We do this by using simulated data that represent two sets of spatially varying processes and examining how well both techniques replicate the known local parameter values. The article finds no support that spatial filtering produces local parameter estimates with superior properties. The results indicate that the original spatial filtering specification is prone to overfitting and is generally inferior to GWR, while an alternative specification that minimizes the mean square error (MSE) of coefficient estimates produces results that are similar to GWR. However, since we generally do not know the true coefficients, the MSE minimizing specification is impractical for applied research.
    June 30, 2017   doi: 10.1111/gean.12133   open full text
  • On the Performance of the Subtour Elimination Constraints Approach for the p‐Regions Problem: A Computational Study.
    Juan Carlos Duque, Mario C. Vélez‐Gallego, Laura Catalina Echeverri.
    Geographical Analysis. June 20, 2017
    The p‐regions is a mixed integer programming (MIP) model for the exhaustive clustering of a set of n geographic areas into p spatially contiguous regions while minimizing measures of intraregional heterogeneity. This is an NP‐hard problem that requires a constant research of strategies to increase the size of instances that can be solved using exact optimization techniques. In this article, we explore the benefits of an iterative process that begins by solving the relaxed version of the p‐regions that removes the constraints that guarantee the spatial contiguity of the regions. Then, additional constraints are incorporated iteratively to solve spatial discontinuities in the regions. In particular we explore the relationship between the level of spatial autocorrelation of the aggregation variable and the benefits obtained from this iterative process. The results show that high levels of spatial autocorrelation reduce computational times because the spatial patterns tend to create spatially contiguous regions. However, we found that the greatest benefits are obtained in two situations: (1) when n/p≥3; and (2) when the parameter p is close to the number of clusters in the spatial pattern of the aggregation variable.
    June 20, 2017   doi: 10.1111/gean.12132   open full text
  • The Old and the New: Qualifying City Systems in the World with Classical Models and New Data.
    Robin Cura, Clémentine Cottineau, Elfie Swerts, Cosmo Antonio Ignazzi, Anne Bretagnolle, Celine Vacchiani‐Marcuzzo, Denise Pumain.
    Geographical Analysis. May 12, 2017
    Zipf's rank‐size rule, lognormal distribution, and Gibrat's urban growth models are considered as summarizing fundamental properties of systems of cities. In this article, they are used as statistical benchmarks for comparing the shapes of urban hierarchies and evolutionary trends of seven systems of cities in the world including BRICS, Europe, and United States. In order to provide conclusions that avoid the pitfalls of too small samples or uncontrolled urban definitions, these models are tested on some 20,000 urban units whose geographically significant delineations were harmonized in each country over 50 years between 1960 and 2010. As a result, if the models appear not always statistically valid, their usefulness is confirmed since the observed deviations from empirical data remain limited and can often be interpreted from the geohistorical context of urbanism proper to each world region. Moreover, the article provides new free software which authorizes the reproducibility of our experiments with our data bases as well as with complementary data.
    May 12, 2017   doi: 10.1111/gean.12129   open full text
  • Modified Moran's I for Small Samples.
    Tomaz Back Carrijo, Alan Ricardo da Silva.
    Geographical Analysis. May 12, 2017
    The most common indicator used to measure spatial dependence is Moran's I proposed by statistician Patrick A. P. Moran in 1950. The index is simple to use and applies the principle of the Pearson correlation coefficient, although it incorporates a proximity measure between elements. However, Moran's I tends to underestimate real spatial autocorrelation when the number of locations are few. This study aims to present a modified version of Moran's I that can measure real spatial autocorrelation even with small samples and check for spatial dependence.
    May 12, 2017   doi: 10.1111/gean.12130   open full text
  • Evaluating Crash Risk in Urban Areas Based on Vehicle and Pedestrian Modeling.
    Itzhak Omer, Victoria Gitelman, Yodan Rofè, Yoav Lerman, Nir Kaplan, Etti Doveh.
    Geographical Analysis. May 03, 2017
    This article presents a method for investigating the spatial distribution of vehicle and pedestrian traffic crashes relative to the volume of vehicle and pedestrian movement in urban areas. This method consists of two phases. First, vehicle and pedestrian traffic volumes on the street network are modeled using a space syntax configurational analysis of the network, land use data, and observed traffic data. Second, crash prediction models are fitted to the traffic crash data, using negative binomial regression models and based on traffic volume estimates and street segment lengths. The method was applied in two areas in Tel Aviv, Israel, which differ in their morphological and traffic characteristics. The case‐studies illustrated the method's capability in identifying hazardous locations on the network and examining relative crash risks. The analysis shows that an increase in vehicle or pedestrian traffic volume tends to be associated with a decrease in relative crash risk. Moreover, the spatial patterns of relative crash risks are associated with the design characteristics of urban space: areas characterized by dense street networks encourage more walking, and are generally safer for pedestrians, while those with longer street segments encourage more driving, are less safe for pedestrians, but safer for vehicles.
    May 03, 2017   doi: 10.1111/gean.12128   open full text
  • Multi‐Domain User‐Generated Content Based Model to Enrich Road Network Data for Multi‐Criteria Route Planning.
    Giti Khoshamooz, Mohammad Taleai.
    Geographical Analysis. April 12, 2017
    By utilizing today's web‐based technologies, people can act as sensors and share their perceptions, emotions and observations in a variety of data forms, such as images, videos, texts, Global Positioning System (GPS) trajectories and maps. These forms are collectively called user‐generated content (UGC). These data are in different domains and have a multi‐modality nature. Although recent efforts have probed the acquisition of local knowledge by using single‐domain UGC data in specific applications, such efforts have not thus far presented a model considering multi‐domain UGC specifically to enrich road network data. This article aims at presenting such a model wherein, with the help of each data domain of UGC, one aspect of people knowledge about the road segment is obtained. These different aspects of knowledge are integrated using a Skyline operator to support multi‐criteria route finding. We name this model ERSBU (enriching road segments based on UGC). In ERSBU, road segments are basic spatial units, and their subjective properties have been extracted by using available UGC. The scenic score for each road segment was computed by using geo‐tagged Panoramio photos. The accessibility level of each road segment to different facilities was calculated based on data captured from Wikimapia and OpenStreetMap. Moreover, for measuring the movement popularity of each road segment, Wikiloc and Everytrail GPS trajectories were utilized. For the implementation of the ERSBU model, Tehran region 6 was considered the case study area. The Evaluation of the results proved that road segments that achieved a high score based on knowledge extracted from UGC also mostly gained top scores by analyzing traditional maps. ERSBU allows users to accomplish more‐qualitative path finding by considering the multi‐view characteristics of road segments.
    April 12, 2017   doi: 10.1111/gean.12124   open full text
  • Nonlinear Multivariate Spatial Modeling Using NLPCA and Pair‐Copulas.
    Gnai Nishani Musafer, Mery Helen Thompson, Rodney C. Wolff, Erhan Kozan.
    Geographical Analysis. April 07, 2017
    A novel geostatistical modeling approach is developed to model nonlinear multivariate spatial dependence using nonlinear principal component analysis (NLPCA) and pair‐copulas. In spatial studies, multivariate measurements are frequently collected at each location. The dependence between such measurements can be complex. In this article, a multivariate geostatistical model is developed that can capture both nonlinear spatial dependence across locations and nonlinear dependence between measurements at a particular location. Nonlinear multivariate dependence between spatial variables is removed using NLPCA. Subsequently, a pair‐copula based model is fitted to each transformed variable to model the univariate nonlinear spatial dependencies. NLPCA and pair‐copulas, within the proposed model, are compared with stepwise conditional transformation (SCT) and conventional kriging. The results show that, for the two case studies presented, the proposed model that utilizes NLPCA and pair‐copulas reproduces nonlinear multivariate structures and univariate distributions better than existing methods based on SCT and kriging.
    April 07, 2017   doi: 10.1111/gean.12126   open full text
  • The Determinants of Regional Freight Transport: A Spatial, Semiparametric Approach.
    Tamás Krisztin.
    Geographical Analysis. April 07, 2017
    In the context of modeling regional freight the four‐stage model is a popular choice. The first stage of the model, freight generation and attraction, however, suffers from three shortcomings: first of all, it does not take spatial dependencies among regions into account, thus potentially yielding biased estimates. Second, there is no clear consensus in the literature as to the choice of explanatory variables. Second, sectoral employment and gross value added are used to explain freight generation, whereas some recent publications emphasize the importance of variables which measure the amount of logistical activity in a region. Third, there is a lack of consensus regarding the functional form of the explanatory variables. Multiple recent studies emphasize nonlinear influences of selected variables. This article addresses these shortcomings by using a spatial variant of the classic freight generation and attraction models combined with a penalized spline framework to model the explanatory variables in a semiparametric fashion. Moreover, a Bayesian estimation approach is used, coupled with a penalized Normal inverse‐Gamma prior structure, to introduce uncertainty regarding the choice and functional form of explanatory variables. The performance of the model is assessed on a real‐world example of freight generation and attraction of 258 European NUTS‐2 level regions, covering 25 European countries.
    April 07, 2017   doi: 10.1111/gean.12125   open full text
  • Basin‐Wide Sediment Grain‐Size Numerical Analysis and Paleo‐Climate Interpretation in the Shiyang River Drainage Basin.
    Yu Li, Chengqi Zhang, Pengcheng Li, Yue Wang.
    Geographical Analysis. April 02, 2017
    Basin‐wide sediment transport affects estimates of basin sediment yield, which is a fundamental scientific issue in drainage basin studies. Many studies have been conducted to examine erosion and deposition rates in drainage networks. In this study, we proposed a new approach using grain‐size standard deviation model of sedimentary samples from different geomorphological units for numerical analysis and paleo‐climate interpretation in the Shiyang River drainage basin, arid China. 1043 sedimentary samples were obtained from the upper reaches, the midstream alluvial plain and the terminal lake area; chronological frames were established based on 58 radiocarbon ages. Grain‐size standard deviation model was introduced to examine sediment components according to grain‐size and transport forces. In addition, transient paleo‐climate simulations, including the Community Climate System Model version 3 and the Kiel models, were synthesized, as well as the results from PMIP 3.0 project, to detect the long‐term climate backgrounds. Totally, we found four major common components, including fine particulates (<2 μm), fine silt (2–20 μm), sandy silt (20–200 μm), coarse sand (>200 μm), from basin‐wide sedimentary samples. The fine particulates and fine silt components exist in all the sedimentary facies, showing long‐term airborne aerosol changes and its transport by suspended load. There are some differences in ranges of sandy silt and coarse sand components, due to lake and river hydrodynamics, as well as the distance with the Gobi Desert. Paleo‐climate simulations have shown that the strong Asian summer monsoon during the transition of the Last Deglaciation and Holocene was conducive to erosion and transport of basin‐wide suspended load, also enhancing sediment sorting effects due to strong lake hydrodynamics. Our findings provide a new approach in research of long‐term basin‐wide sediment transport processes.
    April 02, 2017   doi: 10.1111/gean.12123   open full text
  • Geographically Weighted Extreme Learning Machine: A Method for Space–Time Prediction.
    Min Deng, Wentao Yang, Qiliang Liu.
    Geographical Analysis. April 02, 2017
    Spatial heterogeneity has been regarded as an important issue in space–time prediction. Although some statistical methods of space–time predictions have been proposed to address spatial heterogeneity, the linear assumption makes it difficult for these methods to predict geographical processes accurately because geographical processes always involve complicated nonlinear characteristics. An extreme learning machine (ELM) has the advantage of approximating nonlinear relationships with a rapid learning speed and excellent generalization performance. However, determining how to incorporate spatial heterogeneity into an ELM to predict space–time data is an urgent problem. For this purpose, a new method called geographically weighted ELM (GWELM) is proposed to address spatial heterogeneity based on an ELM in this article. GWELM is essentially a locally varying ELM in which the parameters are regarded as functions of spatial locations, and geographically weighted least squares is applied to estimate the parameters in a local model. The proposed method is used to analyze two groups of different data sets, and the results demonstrate that the GWELM method is superior to the comparative method, which is also developed to address spatial heterogeneity.
    April 02, 2017   doi: 10.1111/gean.12127   open full text
  • A Socio‐Geographic Perspective on Human Activities in Social Media.
    Ding Ma, Mats Sandberg, Bin Jiang.
    Geographical Analysis. February 01, 2017
    Location‐based social media make it possible to understand social and geographic aspects of human activities. However, previous studies have mostly examined these two aspects separately without looking at how they are linked. The study aims to connect two aspects by investigating whether there is any correlation between social connections and users' check‐in locations from a socio‐geographic perspective. We constructed three types of networks: a people–people network, a location–location network, and a city–city network from former location‐based social media Brightkite and Gowalla in the U.S., based on users' check‐in locations and their friendships. We adopted some complexity science methods such as power‐law detection and head/tail breaks classification method for analysis and visualization. Head/tail breaks recursively partitions data into a few large things in the head and many small things in the tail. By analyzing check‐in locations, we found that users' check‐in patterns are heterogeneous at both the individual and collective levels. We also discovered that users' first or most frequent check‐in locations can be the representatives of users' spatial information. The constructed networks based on these locations are very heterogeneous, as indicated by the high ht‐index. Most importantly, the node degree of the networks correlates highly with the population at locations (mostly with R2 being 0.7) or cities (above 0.9). This correlation indicates that the geographic distributions of the social media users relate highly to their online social connections.
    February 01, 2017   doi: 10.1111/gean.12122   open full text
  • Monocentricity and Directional Heterogeneity: A Conditional Parametric Approach.
    Ramon Oller, Joan Carles Martori, Rafa Madariaga.
    Geographical Analysis. December 04, 2016
    The basic monocentric city model implies a decreasing relationship between distance to the Central Business District and outcome variables such as population density, housing prices, land rent, and income. Implicitly, the monocentric model implies directional homogeneity, that is, the distance effect does not depend on the direction in which it is being measured. Only a few works have analyzed the problem of directional heterogeneity. A new conditional parametric approach is introduced to deal with the issue. We study how this approach performs under different spatial patterns comparing with several other methods. An application to the spatial distribution of wage income in the municipality of Madrid is carried out to illustrate the main characteristics of the method and to show the comparative advantages.
    December 04, 2016   doi: 10.1111/gean.12119   open full text
  • Radon Predictions with Geographical Information System Covariates: From Spatial Sampling to Modeling.
    Sandra De Iaco, Sabrina Maggio, Monica Palma.
    Geographical Analysis. November 27, 2016
    Radon (Rn) is a potentially toxic gas in soil which may affect human health. Assessing Rn levels in soil gas usually requires enormous efforts in terms of time and costs, since the sampling protocol is very complex. In most cases, the variable under study is sparsely sampled over the domain and this could affect the reliability of the spatial predictions. For this reason, it is useful to incorporate, into the estimation procedure, some auxiliary variables, correlated with the in soil gas Rn concentrations (primary variable) and more densely available over the domain. On the basis of this latter aspect, it is even better if the covariates are derived from a geographical information system (GIS). In this article, the Rn sampling protocol used during a measurement campaign planned over a risk area is described and the process of deriving GIS covariates considered as secondary information for predicting the primary variable is clarified. Then, multivariate modeling and prediction of the Rn concentrations over the domain of interest are discussed and a comparative study regarding the performance of the prediction procedures is presented. Rn prone areas are also analyzed with respect to urban and school density. All these aspects can clearly support decisions on environmental and human safeguard.
    November 27, 2016   doi: 10.1111/gean.12118   open full text
  • Regional Coverage Maximization: Alternative Geographical Space Abstraction and Modeling.
    Daoqin Tong, Ran Wei.
    Geographical Analysis. November 21, 2016
    Analysis results are often found to vary with the way we abstract geographical space. When continuous geographic phenomena are abstracted, processed, and stored in a digital environment, some level of discretization is often employed. Information loss in a discretization process brings about uncertainty/error, and as a result research findings may be highly dependent on the particular discretization method used. This article examines one spatial problem concerning how to achieve the maximal regional coverage given a limited number of service facilities. Two widely used geographical space abstraction approaches are examined, the point‐based representation and the area‐based representation, and issues associated with each representation scheme are analyzed. To accommodate the limitations of the existing representation schemes, a mixed representation strategy is proposed along with a new maximal covering model. Experiments are conducted to site warning sirens in Dublin, Ohio. Results demonstrate the effectiveness of the mixed representation scheme in finding high‐quality solutions when the regional coverage level is medium or high.
    November 21, 2016   doi: 10.1111/gean.12121   open full text
  • W Function: A New Distance‐Based Measure of Spatial Distribution of Economic Activities.
    Pavel Kukuliač, Jiří Horák.
    Geographical Analysis. November 16, 2016
    Distance‐based methods are applied in various fields of research. In this paper, a new relative distance‐based method, the W function, is introduced. This method contributes to the assessment of spatial patterns of economic activities using the stochastic Monte Carlo simulation, and supplements the typology of distance‐based methods recently drawn up by Marcon and Puech. The capability of the W function is compared with results from the Kd and the recently defined m function methods, which are widely used for monitoring the spatial distribution of economic activities by considering several theoretical and empirical examples. The W function appears to provide more precise estimations of the density of economic activities compared to the m and Kd functions, particularly in cases of complex patterns such as double clustered distribution. It also appears to provide a more accurate evaluation of dispersion.
    November 16, 2016   doi: 10.1111/gean.12120   open full text
  • Prioritizing Disaster Mapping Tasks for Online Volunteers Based on Information Value Theory.
    Yingjie Hu, Krzysztof Janowicz, Helen Couclelis.
    Geographical Analysis. October 13, 2016
    In recent years, online volunteers have played important roles in disaster response. After a major disaster, hundreds of volunteers are often remotely convened by humanitarian organizations to map the affected area based on remote sensing images. Typically, the affected area is divided using a grid‐based tessellation, and each volunteer can select one grid cell to start mapping. While this approach coordinates the efforts of volunteers, it does not differentiate the priorities of different cells. As a result, volunteers may map grid cells in a random order. Due to the spatial heterogeneity, different cells may contain geographic information that is of different value to emergency responders. Ideally, cells that potentially contain more valuable information should be assigned higher priority for mapping. This article presents an analytical framework for prioritizing the mapping of cells based on the values of information contained in these cells. Our objective is to provide guidance for online volunteers so that potentially more important cells are mapped first. We present a method that is based on information value theory and focus on road networks. We apply this method to a number of simulated scenarios and to a real disaster mapping case from the 2015 Nepal earthquake.
    October 13, 2016   doi: 10.1111/gean.12117   open full text
  • Can Dasymetric Mapping Significantly Improve Population Data Reallocation in a Dense Urban Area?
    Jose M. Pavía, Isidro Cantarino.
    Geographical Analysis. August 30, 2016
    The issue of reallocating population figures from a set of geographical units onto another set of units has received a great deal of attention in the literature. Every other day, a new algorithm is proposed, claiming that it outperforms competitor procedures. Unfortunately, when the new (usually more complex) methods are applied to a new data set, the improvements attained are sometimes just marginal. The relationship cost‐effectiveness of the solutions is case‐dependent. The majority of studies have focused on large areas with heterogeneous population density distributions. The general conclusion is that as a rule more sophisticated methods are worth the effort. It could be argued, however, that when we work with a variable that varies gradually in relatively homogeneous small units, simple areal weighting methods could be sufficient and that ancillary variables would produce marginal improvements. For the case of reallocating census data, our study shows that, even under the above conditions, the most sophisticated approaches clearly yield the better results. After testing fourteen methods in Barcelona (Spain), the best results are attained using as ancillary variable the total dwelling area in each residential building. Our study shows the 3‐D methods as generating the better outcomes followed by multiclass 2‐D procedures, binary 2‐D approaches and areal weighting and 1‐D algorithms. The point‐based interpolation procedures are by far the ones producing the worst estimates.
    August 30, 2016   doi: 10.1111/gean.12112   open full text
  • Extending Moran's Index for Measuring Spatiotemporal Clustering of Geographic Events.
    Jay Lee, Shengwen Li.
    Geographical Analysis. June 26, 2016
    Moran's Index for spatial autocorrelation and localized index for spatial association have been widely applied in many research fields as the first step to explore and assess the spatial dependency in a set of geographic events. This article presents extensions to the equations for calculating global and localized spatial autocorrelation so to include the temporal attribute values of the geographic events being analyzed. The extended equations were successfully implemented and tested with a real world data set. In addition, simulated data sets were used to reveal how the extended equations performed. Beyond the usefulness of the extended equations, we suggest that care be taken with regard to assessing spatiotemporal patterns under the normality and randomization assumptions as different outcomes from different assumptions would require different approaches for interpretation.
    June 26, 2016   doi: 10.1111/gean.12106   open full text
  • Using an Evolutionary Algorithm in Multiobjective Geographic Analysis for Land Use Allocation and Decision Supporting.
    Zohreh Masoumi, Jamshid Maleki, Mohammad Sadi Mesgari, Ali Mansourian.
    Geographical Analysis. June 24, 2016
    Usually, allocation of resources is an optimization problem which involves a variety of conflicting economic, social, and ecological objectives. In such a process, advanced geographic analyst tool for manipulation of spatial data and satisfaction of multiple objectives is essential to the success of decision‐making. The present research intends to demonstrate the application of a multiobjective optimization method based on NSGA‐II (we call it HNSGA‐II), along with Geographical Information System (GIS) to select suitable sites for the establishment of large industrial units. Having defined the elements of HNSGA‐II for the site selection of industrial units, the method is tested on the data of Zanjan province, Iran, as the case study. The results showed that the proposed approach can easily find a variety of optimized solutions, giving the decision‐makers the possibility to opt for the most propitious solution. Using this method, the achievement level regarding each objective function can be studied for any of the nondominated solutions.
    June 24, 2016   doi: 10.1111/gean.12111   open full text
  • Small Area Population Estimation: Estimating Population Size at Ward Level 2014 in South Africa.
    Eric O. Udjo.
    Geographical Analysis. June 22, 2016
    The census is the traditional source of population figures at various levels. Census figures however are technically outdated immediately they are released because planners require figures for the present and possibly for future dates. In an attempt to meet this demand different organisations and researchers produce population estimates and projections. These estimates however are usually at higher geographical levels and often do not meet the planning needs of administrators at lower geographical levels. This study extends a top‐down estimation method to an African country by estimating the mid‐2014 population at ward level in South Africa. The study used the 2011 South Africa Census to estimate current levels of fertility, mortality as well as current trends in net migration at a higher geographical level. Historical trends in fertility and mortality were based on the 1996 Census, 1997 and 1998 October Household Surveys as well as the 2007 Community Survey data. The results indicate that that 20 of the largest wards as at mid‐2014 were located in South Africa's metropolitan areas. Nineteen of the 20 largest wards are currently growing at a rate of over 4% per annum and if this trend continues, eighteen of these wards will double their current population size in less than 15 years.
    June 22, 2016   doi: 10.1111/gean.12110   open full text
  • Persistence of Crime Hot Spots: An Ordered Probit Analysis.
    Li He, Antonio Páez, Desheng Liu.
    Geographical Analysis. June 20, 2016
    The temporal persistence of crime hot spots is recognized as a valuable indicator of consistent problem areas. The current literature has not adequately addressed the mechanisms that perpetuate or interrupt persistent crime hot spots. Investigating the persistence of violent crime hot spots in Columbus, Ohio, from 1994 to 2002, this study fills a gap in the literature by identifying neighborhood structural correlates that drive the persistence of hot spots. Specifically, this study identifies yearly crime hot spots, and estimates an ordered probit model to explore the neighborhood structural determinants. The results indicate that socio‐economic factors, identified from a synthesis of social disorganization theory and routine activity theory, significantly correlate with persistent patterns of violent crime hot spots. This gives evidence that a combination of the two ruling spatial theories of crime provides an applicable framework for understanding the temporal dimension of violent crime hot spots. By identifying the factors that contribute to the persistence of hot spots of crime, insights gained from the results can help to inform focused crime prevention efforts.
    June 20, 2016   doi: 10.1111/gean.12107   open full text
  • p‐Functional Clusters Location Problem for Detecting Spatial Clusters with Covering Approach.
    Kamyoung Kim, Yongwan Chun, Hyun Kim.
    Geographical Analysis. June 20, 2016
    Regionalization or districting problems commonly require each individual spatial unit to participate exclusively in a single region or district. Although this assumption is appropriate for some regionalization problems, it is less realistic for delineating functional clusters, such as metropolitan areas and trade areas where a region does not necessarily have exclusive coverage with other regions. This paper develops a spatial optimization model for detecting functional spatial clusters, named the p‐functional clusters location problem (p‐FCLP), which has been developed based on the Covering Location Problem. By relaxing the complete and exhaustive assignment requirement, a functional cluster is delineated with the selective spatial units that have substantial spatial interaction. This model is demonstrated with applications for a functional regionalization problem using three journey‐to‐work flow datasets: (1) among the 46 counties in South Carolina, (2) the counties in the East North Central division of the US Census, and (3) all counties in the US. The computational efficiency of p‐FCLP is compared with other regionalization problems. The computational results show that detecting functional spatial clusters with contiguity constraints effectively solves problems with optimality in a mixed integer programming (MIP) approach, suggesting the ability to solve large instance applications of regionalization problems.
    June 20, 2016   doi: 10.1111/gean.12109   open full text
  • Bi‐objective Model for Optimal Number and Size of Circular Facilities.
    Masashi Miyagawa.
    Geographical Analysis. June 20, 2016
    This article presents a bi‐objective model for determining the number and size of finite size facilities. The objectives are to minimize both the average closest distance to facilities and the probability that a random line intersects facilities. The former represents the accessibility of customers, whereas the latter represents the interference to travelers. The average closest distance and the probability of intersecting facilities are derived for circular facilities randomly distributed in a circular city. The analytical expressions for the average closest distance and the probability of intersecting facilities demonstrate how the number and size of facilities affect the accessibility of customers and the interference to travelers. The model focuses on the tradeoff between the accessibility and interference, and the tradeoff curve provides planners with alternatives for the number and size of facilities.
    June 20, 2016   doi: 10.1111/gean.12108   open full text
  • Analyzing the Spatial Dynamics of Deforestation in Brazilian Amazon.
    Daniel de Alencastro Bouchardet, Alexandre Alves Porsse, Romano Timofeiczyk Junior.
    Geographical Analysis. May 23, 2016
    Historically, development in Brazilian Amazon was promoted by permits of deforestation under soft territory control or supervision. However, due to the importance of this biome for biodiversity and ecosystem balance in a global perspective, Brazilian's government has tightened deforestation control. This work investigates the spatial pattern of deforestation in a cross‐section and time perspective using global and local spatial data analysis. Global results indicate the existence of high spatial correlation and that deforestation holds concentrated across space, despite the efficacy of policy mechanisms adopted for controlling and reducing the level of deforestation in Legal Amazon. Furthermore, local results support the hypothesis of high spillover effects. Considering the spatial analysis results, we highlight some implications for policy design aiming deforestation control.
    May 23, 2016   doi: 10.1111/gean.12105   open full text
  • Empirical Zoning Distributions for Small Area Data.
    Sandy Burden, David Steel.
    Geographical Analysis. May 19, 2016
    It is well established that using data summaries for a set of geographic areas or zones to estimate the parameters of a statistical model, commonly called ecological inference, frequently leads to the modifiable area unit problem (MAUP). In this article, the zoning effect of the MAUP is investigated for a range of scales. A zoning distribution is defined, and then used to characterize the zoning effect for parameter estimates from ecological analyses. Zone‐independent parameter estimates are obtained using the mean of the zoning distribution, and assessed using the variance of the zoning distribution. Zoning distributions are illustrated for parameter estimates from two ecological regression models at multiple scales using Australian National Health Survey data. For both a continuous response variable and a binary response variable, the empirical zoning distributions are unimodal, relatively symmetrical with appreciable variation, even when based on a large number of zones. The “ecological mean,” or expected value of the empirical zoning distribution at each scale, displays systematic variation with scale and the zoning distribution variance also depends on scale. The results demonstrate that the zoning effect should not be ignored, and the sensitivity of ecological analysis results to the analysis zones should be assessed.
    May 19, 2016   doi: 10.1111/gean.12104   open full text
  • Comparison of GIS‐Based Logic Scoring of Preference and Multicriteria Evaluation Methods: Urban Land Use Suitability.
    Bryn Montgomery, Suzana Dragićević.
    Geographical Analysis. May 02, 2016
    Multi‐criteria evaluation (MCE) methods are useful tools to evaluate the land suitability for various uses and assist in the effective management of available land. Many common GIS‐based MCE methods, such as analytical hierarchy process (AHP), ordered weighted averaging (OWA), and a combination of AHP and OWA methods (AHP–OWA) are not able to fully represent all the logic that constitute a wide range of human decision‐making reasoning. Consequently, improved GIS‐based MCE methods such as Logic Scoring of Preference (LSP) method are needed. The main objectives of this study are to: (1) implement the GIS‐based LSP method for land suitability evaluation and (2) compare qualitatively and quantitatively the suitability maps generated by LSP and three GIS‐based MCE methods. This study was implemented with data sets from Boulder County, Colorado, USA for the case study of the urban land suitability evaluation. The qualitative properties of MCE methods and the Receiver Operating Characteristic (ROC) statistics were used as comparison metrics. The results indicate that soft computing methods and particularly LSP performed the best among GIS‐based MCE methods for the urban land use application.
    May 02, 2016   doi: 10.1111/gean.12103   open full text
  • Spatial Cluster Detection in Spatial Flow Data.
    Ran Tao, Jean‐Claude Thill.
    Geographical Analysis. April 28, 2016
    As a typical form of geographical phenomena, spatial flow events have been widely studied in contexts like migration, daily commuting, and information exchange through telecommunication. Studying the spatial pattern of flow data serves to reveal essential information about the underlying process generating the phenomena. Most methods of global clustering pattern detection and local clusters detection analysis are focused on single‐location spatial events or fail to preserve the integrity of spatial flow events. In this research we introduce a new spatial statistical approach of detecting clustering (clusters) of flow data that extends the classical local K‐function, while maintaining the integrity of flow data. Through the appropriate measurement of spatial proximity relationships between entire flows, the new method successfully upgrades the classical hot spot detection method to the stage of “hot flow” detection. Several specific aspects of the method are discussed to provide evidence of its robustness and expandability, such as the multiscale issue and relative importance control, using a real data set of vehicle theft and recovery location pairs in Charlotte, NC.
    April 28, 2016   doi: 10.1111/gean.12100   open full text
  • Measuring Relative Sustainability of Regions Using Regional Sustainability Assessment Methodology.
    Sergiy Smetana, Christine Tamásy, Alexander Mathys, Volker Heinz.
    Geographical Analysis. April 18, 2016
    Scientists analyze sustainability at the regional level with a combination of multiple indicators which reflect different characteristics of regions without combining the results in a single comparative unit. Moreover, the assessment of interdependencies between different characteristics requires experts' analyses, which makes sustainability analysis subjective, time consuming, and limited in use. This article analyzes the relative sustainability of subnational level regions through the application of regional sustainability assessment methodology (RSAM) based on accounting of resources capital and its internal and external transfers. This approach allows for assessment of regional sustainability as a function of resource quantity, quality, and interchangeability. The comparison of the two case study regions presented in the paper indicates the difference between a more sustainable region and a region of “weak sustainability.” First, the article indicates the discussion of the relevant geographic, economic, and social literature for both sustainability assessment and regional comparison. This discussion is followed by a conceptual representation of proposed RSAM and its application to various regions. Next, the article covers the data used and applied methods to test the proposed methodology and compare the two case study regions. The article concludes with a discussion of findings and recommendations for further application and testing.
    April 18, 2016   doi: 10.1111/gean.12102   open full text
  • Designing Reliable Center Systems: A Vector Assignment Center Location Problem.
    Ting L. Lei.
    Geographical Analysis. April 12, 2016
    The p‐center problem is one of the most important models in location theory. Its objective is to place a fixed number of facilities so that the maximum service distance for all customers is as small as possible. This article develops a reliable p‐center problem that can account for system vulnerability and facility failure. A basic assumption is that located centers can fail with a given probability and a customer will fall back to the closest nonfailing center for service. The proposed model seeks to minimize the expected value of the maximum service distance for a service system. In addition, the proposed model is general and can be used to solve other fault‐tolerant center location problems such as the (p, q)‐center problem using appropriate assignment vectors. I present an integer programming formulation of the model and computational experiments, and then conclude with a summary of findings and point out possible future work.
    April 12, 2016   doi: 10.1111/gean.12101   open full text
  • A Bi‐objective Optimization Model for Designing Safe Walking Routes for School Children.
    Ken‐ichi Tanaka, Ryuhei Miyashiro, Yuichiro Miyamoto.
    Geographical Analysis. March 08, 2016
    This study uses a mathematical optimization approach to design safe walking routes from school to home for children. Children are thought to be safer when walking together in groups rather than alone. Thus, we assume that the risk of walking along a given road segment in a group is smaller than that of walking the same segment alone. At the same time, the walking route between school and home for each child should not deviate substantially from the shortest route. We propose a bi‐objective model that minimizes both the total risk (particularly, the total distance walked alone) and the total walking distance for all children. We present an integer programming formulation of the proposed problem and apply this formulation to two instances based on actual road networks. We obtain Pareto optimal solutions using a mathematical programming solver and analyze the characteristics of the solutions and their potential applicability to real situations. The results show that the proposed model produces much better solutions compared with the solution where each child walks along the shortest path from school to home. In some optimal solutions, only a small deviation from the shortest path results in a dramatic reduction of the risk objective.
    March 08, 2016   doi: 10.1111/gean.12095   open full text
  • Spatial Distribution of City Tweets and Their Densities.
    Bin Jiang, Ding Ma, Junjun Yin, Mats Sandberg.
    Geographical Analysis. February 18, 2016
    Social media outlets such as Twitter constitute valuable data sources for understanding human activities in the virtual world from a geographic perspective. This article examines spatial distribution of tweets and densities within cities. The cities refer to natural cities that are automatically aggregated from a country's small street blocks, so called city blocks. We adopted street blocks (rather than census tracts) as the basic geographic units and topological center (rather than geometric center) to assess how tweets and densities vary from the center to the peripheral border. We found that, within a city from the center to the periphery, the tweets first increase and then decrease, while the densities decrease in general. These increases and decreases fluctuate dramatically, and differ significantly from those if census tracts are used as the basic geographic units. We also found that the decrease of densities from the center to the periphery is less significant, and even disappears, if an arbitrarily defined city border is adopted. These findings prove that natural cities and their topological centers are better than their counterparts (conventionally defined cities and city centers) for geographic research. Based on this study, we believe that tweet densities can be a good surrogate of population densities. If this belief is proved to be true, social media data could help solve the dispute surrounding exponential or power function of urban population density.
    February 18, 2016   doi: 10.1111/gean.12096   open full text
  • Latent trajectory models for space‐time analysis: An application in deciphering spatial panel data.
    Li An, Ming‐Hsiang Tsou, Brian H. Spitzberg, Dipak K. Gupta, J. Mark Gawron.
    Geographical Analysis. February 09, 2016
    This article introduces latent trajectory models (LTMs), an approach often employed in social sciences to handle longitudinal data, to the arena of GIScience, particularly space‐time analysis. Using the space‐time data collected at county level for the whole United States through webpage search on the keyword “climate change,” we show that LTMs, when combined with eigenvector filtering of spatial dependence in data, are very useful in unveiling temporal trends hidden in such data: the webpage‐data derived popularity measure for climate change has been increasing from December 2011 to March 2013, but the increase rate has been slowing down. In addition, LTMs help reveal potential mechanisms behind observed space‐time trajectories through linking the webpage‐data derived popularity measure about climate change to a set of socio‐demographic covariates. Our analysis shows that controlling for population density, greater drought exposure, higher percent of people who are 16 years old or above, and higher household income are positively predictive of the trajectory slopes. Higher percentages of Republicans and number of hot days in summer are negatively related to the trajectory slopes. Implications of these results are examined, concluding with consideration of the potential utility of LTMs in space‐time analysis and more generally in GIScience.
    February 09, 2016   doi: 10.1111/gean.12097   open full text
  • Spatial Distribution of Human Population in France: Exploring the Modifiable Areal Unit Problem Using Multifractal Analysis.
    François Sémécurbe, Cécile Tannier, Stéphane G. Roux.
    Geographical Analysis. February 08, 2016
    Case studies in geography are strongly dependent on the size of the spatial units used for the analysis. This has been expressed as the Modifiable Areal Unit Problem (MAUP): whatever the phenomenon under consideration, it is impossible to identify a single spatial partition that would be most appropriate to analyze it. In this respect, multifractal analysis may be an interesting tool for geographers. It integrates not just a series of nested spatial resolutions, as fractal analysis does, but also a series of points of view about the quantity of information contained in each spatial unit. In this article, we first expose the mathematical bases of multifractal analysis and we describe how it applies to geographical analyses. We insist on the mathematical notion of dimension, which allows us to describe how multifractal parameters can be used to quantify the MAUP. Then, we use the method to characterize the spatial distribution of population density in France. The main result is a typology map of population density that uses the MAUP as a descriptive tool. This map allows the joint identification of several phenomena: the main cities, the rural settlement patterns, and several types of periurban settlement patterns.
    February 08, 2016   doi: 10.1111/gean.12099   open full text
  • The SpatialARMED Framework: Handling Complex Spatial Components in Spatial Association Rule Mining.
    Thi Hong Diep Dao, Jean‐Claude Thill.
    Geographical Analysis. February 06, 2016
    Recent research has identified spatial association rule (SAR) mining as a promising technique for geographic pattern mining and knowledge discovery. Nevertheless, important spatial components embedded in the studied phenomenon, in particular complex spatial functional relations such as neighborhood effects and spatial spillover effects have largely been neglected. This article unravels this specific problem to enhance the effective application of SAR mining practices in spatial data analytics. The main discussion focuses on the specification of complex spatial components by means of spatial dependence properties of the data and on how to integrate them in the process of SAR mining. A comprehensive framework dubbed SpatialARMED is proposed for the effective extraction of spatial patterns. The framework is then showcased through its application to crime analysis.
    February 06, 2016   doi: 10.1111/gean.12094   open full text
  • Quantifying Animal Trajectories Using Spatial Aggregation and Sequence Analysis: A Case Study of Differentiating Trajectories of Multiple Species.
    Peng Gao, John A. Kupfer, Xi Zhu, Diansheng Guo.
    Geographical Analysis. February 06, 2016
    The increasing availability of telemetry data with high spatial and temporal resolution promises to greatly advance scientific understandings of the movement patterns of individual organisms across space and time. The amount of data provided by such methods, however, can be challenging to analyze and interpret. In this study, we present a new approach for analyzing animal movements that aggregates telemetry locations into spatial clusters and extracts the information from sequences formed by individuals passing through these spatial clusters. We applied this integrated approach of spatial aggregation and sequence analysis to quantify and compare trajectories of cattle (Bos taurus), mule deer (Odocoileus hemionus), and elk (Cervus elaphus) tracked by automated telemetry at the Starkey Experimental Forest and Range in northeastern Oregon, USA. Our approach effectively differentiated movement patterns of the three species. It provides a useful mean of quantifying movement patterns of species in a landscape.
    February 06, 2016   doi: 10.1111/gean.12098   open full text
  • Estimating Rapidity of Change in Complex Urban Systems: A Multidimensional, Local‐Scale Approach.
    Luca Salvati, Pere Serra.
    Geographical Analysis. November 19, 2015
    This study illustrates an exploratory approach based on a Multiway Factor Analysis (MFA) to estimate rapidity of change in complex urban systems, based on “fast” and “slow” variables. The proposed methodology was applied to 18 socioeconomic indicators of long‐term (1960–2010) transformations in 115 municipalities of Athens’ metropolitan area (Greece), including demography, land‐use/planning, and urban form and functions. Athens was regarded as a dynamic urban area with diversified structures and functions at the local scale, expanding through a self‐organized pattern rather than a centralized planning strategy. Athens’ urban system was described using nine supplementary (topographic and territorial) variables and 30 independent indicators assessing the local context in recent times. Exploratory data analysis found an increasing connectedness and redundancy among socioeconomic indicators during the phase of largest urban expansion (1960–1990). Only the rate of population growth was classified as a “fast” variable for all five decades investigated. The overall rapidity of change was higher in 1960–1970, 1980–1990, and 2000–2010, decades that coincided with specific phases of urban expansion driven by migration inflow, second‐home suburbanization, and Olympic games, respectively. Rapidity of change was high for functional indicators during all five decades studied, while demography indicators changed more rapidly in the first three decades and land‐use/planning indicators in the last two decades. Rapidity of change was highest in peri‐urban municipalities with a highly diversified economic structure dominated by industry. Our methodology provides a comprehensive overview of the transformations of a complex urban system, quantifying low‐level indicators that are rarely assessed in the mainstream literature on urban studies. These results may contribute to design policies addressing complexity and promoting resilience in expanding metropolitan areas.
    November 19, 2015   doi: 10.1111/gean.12093   open full text
  • Destination Choice of Athenians: An Application of Geographically Weighted Versions of Standard and Zero Inflated Poisson Spatial Interaction Models.
    Stamatis Kalogirou.
    Geographical Analysis. November 12, 2015
    The main aim of this article is to combine recent developments in spatial interaction modeling to better model and explain spatial decisions. The empirical study refers to migration decisions made by internal migrants from Athens, Greece. To achieve this, geographically weighted versions of standard and zero inflated Poisson (ZIP) spatial interaction models are defined and fit. In the absence of empirical studies for the effect of potential determinants on internal migration decisions in Greece and the presence of an excessive number of zero migration flows among municipalities in Greece, this article provides empirical evidence for the power of the proposed Geographically Weighted ZIP regression method to better explain destination choices of Athenian internal migrants. We also discuss statistical inference issues in relation to the application of the proposed regression techniques.
    November 12, 2015   doi: 10.1111/gean.12092   open full text
  • Graphical Inference in Geographical Research.
    Holly M. Widen, James B. Elsner, Stephanie Pau, Christopher K. Uejio.
    Geographical Analysis. September 16, 2015
    Graphical inference, a process refined by Buja et al., can be a useful tool for geographers as it provides a visual and spatial method to test null hypotheses. The core idea is to generate sample datasets from a null hypothesis to visually compare with the actual dataset. The comparison is performed from a line‐up of graphs where a single graph of the actual data is hidden among multiple graphs of sample data. If the real data is discernible, the null hypothesis can be rejected. Here, we illustrate the utility of graphical inference using examples from climatology, biogeography, and health geography. The examples include inferences about location of the mean, change across space and time, and clustering. We show that graphical inference is a useful technique to answer a broad range of common questions in geographical datasets. This approach is needed to avoid the common pitfalls of “straw man” hypotheses and “p‐hacking” as datasets become increasingly larger and more complex.
    September 16, 2015   doi: 10.1111/gean.12085   open full text
  • Spatial Filtering for Identifying a Shortest Path Around Obstacles.
    Insu Hong, Alan T. Murray, Levi J. Wolf.
    Geographical Analysis. September 10, 2015
    The shortest path between two locations is crucial for location modeling, spatial analysis, and wayfinding in complex environments. When no transportation system or network exists, continuous space movement adds substantial complexity to identifying a best path as there are increased travel options as well as barriers inhibiting potential movement. To derive the shortest path, various methods have been developed. Recent work has attempted to exploit spatial knowledge and geographic information system functionality, representing significant advantages over existing methods. However, a high density of obstacles increases computational complexity making real‐time solution difficult in some situations. This article presents a spatial filtering method to enhance Euclidean shortest path derivation in complex environments. The new approach offers substantial computational improvement while still guaranteeing an optimal path is found. Application results demonstrate the effectiveness of the approach and its comparative superiority.
    September 10, 2015   doi: 10.1111/gean.12086   open full text
  • Two‐regime Pattern in Human Mobility: Evidence from GPS Taxi Trajectory Data.
    Zhong Zheng, Soora Rasouli, Harry Timmermans.
    Geographical Analysis. September 10, 2015
    Research on complex systems has identified various aggregate relationships in phenomena that describe these systems. Travel length has been characterized by negative power distributions. Controversy, however, exists over whether mobility patterns can be modeled in terms of a simple power law (Lévy flight model) or that more complicated power laws (exponential power law, truncated Pareto) are required. This study concentrates on two issues: testing the validity of exponential power laws and truncated Pareto distributions in urban systems to describe aggregate mobility patterns, and examining differences in mobility patterns for different travel purposes. The article describes the results of an analysis of Global Positioning System (GPS) taxi trajectory data, collected in Guangzhou, China, to identify mobility patterns in the city. The least squares statistic is used to estimate the parameters of the distribution functions. Results suggest that a fusion of functions, based on an exponential power law and a truncated Pareto distribution, represents the travel time distribution best. Moreover, the findings of this study indicate different mobility patterns to exist for different travel purposes.
    September 10, 2015   doi: 10.1111/gean.12087   open full text
  • The Multiple Testing Issue in Geographically Weighted Regression.
    Alan Ricardo da Silva, A. Stewart Fotheringham.
    Geographical Analysis. September 10, 2015
    This article describes the problem of multiple testing within a Geographically Weighted Regression framework and presents a possible solution to the problem which is based on a family‐wise error rate for dependent processes. We compare the solution presented here to other solutions such as the Bonferroni correction and the Byrne, Charlton, and Fotheringham proposal which is based on the Benjamini and Hochberg False Discovery Rate. We conclude that our proposed correction is superior to others and that generally some correction in the conventional t‐test is necessary to avoid false positives in GWR.
    September 10, 2015   doi: 10.1111/gean.12084   open full text
  • Do Spatial Interdependencies Matter in Italian Regional Specialization?
    Rita De Siano, Marcella D'Uva.
    Geographical Analysis. April 13, 2014
    This article summarizes and evaluates the effects of spatial interdependencies in Italian regional specialization over the period 1995–2006. First, we perform an exploratory spatial data analysis (ESDA), and then we estimate a spatial panel data model built according to the new economic geography theory. ESDA reveals positive spatial interdependence overall and detects hot spots in the north and cold spots in the south for all sectors, but agriculture shows the reverse. Similarly, an econometric investigation furnishes evidence of the presence of spillover effects, implying that the determinants of the specialization of a region influence its neighbors' specialization. Este artículo resume y evalúa los efectos de las interdependencias espaciales en la especialización regional italiana durante el período 1995–2006. En primer lugar, se realiza un análisis exploratorio de datos espaciales (Exploratory Spatial Data Analysis‐ESDA), y luego se estima un modelo de datos de panel espacial construido de acuerdo a la teoría de la nueva geografía económica (New Economic Geography‐NEG). ESDA revela la interdependencia espacial positiva global en los datos y detecta zonas de concentración de incidencia alta (hot spots) en los el norte y zonas concentración de incidencia baja o nula (cold spots) en el sur para todos los sectores económicos excepto el caso de la agricultura. Del mismo modo el estudio realiza un análisis econométrico que demuestra la presencia de efectos de desbordamiento espacial (spillovers), lo cual implica que los factores determinantes de la especialización de una región influyen en la especialización de sus vecinos. 本文对1995‐2006年间意大利区域专业化的空间依赖性效果进行了总结和评价。首先进行了探索性空间数据分析(ESDA),然后对根据新经济地理理论构建的一种空间面板数据模型进行了评价。ESDA揭示了全局的正向空间相互依赖性及北部热点区和南部冷点区的分布特征,但农业的分布特征却相反。同样地,一项计量经济调查提供了溢出效应存在的证据,同时表明了区域专业化的决定因素影响其邻近区域的专业化。
    April 13, 2014   doi: 10.1111/gean.12035   open full text
  • Analyzing Space–Time Crime Incidents Using Eigenvector Spatial Filtering: An Application to Vehicle Burglary.
    Yongwan Chun.
    Geographical Analysis. April 13, 2014
    Poisson models generally are utilized in analyzing spatial patterns of crime count data. When spatial autocorrelation is present, these models are extended to account for it. Among various methods, eigenvector spatial filtering (ESF) furnishes an efficient means of analysis. However, because space–time crime data have temporal components as well as spatial components, Poisson models need to be further adjusted to reflect the two types of components simultaneously. This article discusses how the ESF method can be utilized to model space–time crime data, extending the generalized linear mixed model specification for it. This approach is illustrated with an application to space–time vehicle burglary incidents in the city of Plano, Texas, during 2004–2009. Los modelos de Poisson generalmente se utilizan en el análisis de los patrones espaciales de los datos de recuento de crimen. Cuando hay autocorrelación espacial, estos modelos son modificados para dar cuenta de ello. Entre los diversos métodos existentes, el método Eigenvector (autovector, vector propio) de filtrado espacial (Eigenvector Spatial Filtering‐ESF) proporciona un medio eficaz para dicho análisis. Sin embargo, dado que los datos de criminalidad espacio‐temporales tienen tanto componentes temporales como espaciales, los modelos tipo Poisson requieren de un ajuste adicional para reflejar ambos tipos de componentes de manera simultánea. El artículo presente expone cómo el método ESF puede ser utilizado para modelar datos espacio‐temporales sobre delitos mediante la modificación del modelo mixto lineal generalizado (Generalized Linear Mixed Model‐GLMM). El procedimiento propuesto se ilustra con el caso de incidentes espacio‐temporales de robos de vehículos en la ciudad de Plano, Texas, durante 2004–2009. 泊松模型一般用于犯罪计数数据的空间模式分析中,当空间自相关关系呈现时,这类模型可扩展以解释潜在的分布特征。在各种模型中,特征向量空间滤波(ESF)提供了一种有效的分析方法。然而,由于时空犯罪数据包含时间和空间组分,因此泊松模型需要进一步调整以同时反映这两种不同类型的数据。本文讨论了如何利用特征向量空间滤波(ESF)模型对时空犯罪数据进行建模,并采用扩展广义线性混合模型(GLMM)进行规范。最后,以德克萨斯州普莱诺市2004‐2009年的车辆盗窃案数据进行了实证验证。
    April 13, 2014   doi: 10.1111/gean.12034   open full text
  • Food Inflation in the European Union: Distribution Analysis and Spatial Effects.
    Angelos Liontakis, Dimitris Kremmydas.
    Geographical Analysis. April 13, 2014
    In the European Union (EU), homogenous inflation forces are expected to prevail because of increased economic integration, especially after the creation of a single currency area. This expectation is directly related to the issue of inflation convergence, which has gained increasing attention by both academics and policy makers in Europe. Although the examination of core inflation is of great importance for macroeconomic policy, the role of disaggregate inflation indices, and especially food inflation, has also been emphasized in the literature. However, the issue of food inflation convergence has been largely ignored to date in empirical studies. This study explores the evolving distribution of food inflation rates in the EU‐25 member states using distribution dynamics analysis and covering the period from January 1997 to March 2011. This analysis assumes that each country represents an independent observation, providing unique information that can be used to estimate the transition dynamics of inflation. We show that spatial autocorrelation prevails inside the EU‐25, and, therefore, the independency assumption is violated. To ensure spatial independence, the Getis spatial filter is implemented prior to a distribution dynamics analysis. The results of this analysis confirm the existence of convergence trends, which are even clearer after the spatial filtering procedure, indicating, on the one hand, the influence of spatial effects on food inflation and, on the other hand, the effectiveness of the Getis spatial filtering technique. En la Unión Europea (UE), se espera que las fuerzas de inflación homogéneas prevalezcan debido a la mayor integración económica, sobre todo después de la creación de la zona de moneda única. Esta expectativa se relaciona directamente con el tema de la convergencia de la inflación, que ha ganado cada vez más atención por parte de los investigadores académicos y los decisores políticos europeos. Aunque el análisis de la inflación subyacente es de gran importancia para la política macroeconómica, el papel de los índices de inflación a niveles desagregados, sobre todo en el caso de la inflación de alimentos, ha sido un tema enfatizado por la literatura especializada. Sin embargo, la cuestión de la convergencia de la inflación de alimentos carece hasta la fecha de estudios empíricos. El artículo presente estudia la evolución de la distribución de las tasas de inflación de alimentos en los estados miembros de la UE‐25, utilizando el método de análisis de la dinámica de distribución (distribution dynamic analysis) y abarca el período comprendido entre enero de 1997 a marzo de 2011. Este análisis supone que cada país representa una observación independiente que proporciona información única que se puede utilizada para estimar la dinámica de transición inflacionaria. El presente estudio demuestra que la autocorrelación espacial prevalece dentro de los estados UE‐ 25, y por lo tanto la hipótesis de independencia estadística de las observaciones es violada. Para garantizar la independencia espacial, el método de filtrado espacial Getis (Getis Spatial Filter) es implementado antes de proceder con el análisis de la dinámica de distribución. Los resultados del análisis confirman la existencia de las tendencias de convergencia, que son aún más claras después de la aplicación del filtrado espacial. Estos resultados evidencian por un lado, la influencia de los efectos espaciales en la inflación de alimentos, y por otro lado, la eficacia de la técnica de filtrado espacial de Getis. 在欧盟中,由于区域经济一体化进程的推进,同质商品的通货膨胀盛行,特别是单一货币区域建立后,该趋势更为明显。这种演化态势和通货膨胀的收敛问题直接相关,已经引起了欧洲学术界和政策制定者的关注。虽然核心通胀的检测对宏观经济政策十分重要,但是分解通胀指数的作用,尤其是本文所强调的食品通胀也有文献中提及。然而,食品通胀的收敛问题却在实证研究中很大程度上被忽视。本文利用分布动力学方法对欧盟25国1997年1月至2011年3月的食品通胀率的演化分布进行分析。假设每个国家代表一个独立的观察个体并提供唯一的信息,可用于估计通胀的转变动态,研究发现空间自相关在欧盟25国中普遍存在,因此独立假设不成立。为了保证空间独立性,在进行分布动力学分析之前,先使用Getis空间滤波技术进行处理。分析结果证实了收敛趋势的存在,且该趋势在空间滤波程序处理后更为明显。它一方面显示出食品通胀空间效应的影响,另一方面表现出Getis空间滤波技术的有效性。
    April 13, 2014   doi: 10.1111/gean.12033   open full text
  • Weighted‐Average Least Squares Applied to Spatial Econometric Models: A Monte Carlo Investigation.
    Hajime Seya, Morito Tsutsumi, Yoshiki Yamagata.
    Geographical Analysis. April 13, 2014
    Recently, model averaging techniques have been employed widely in empirical investigations as an alternative to the conventional model selection procedure, a procedure criticized because it disregards a major component of uncertainty, namely, uncertainty regarding the model itself, and, thus, it leads to the underestimation of uncertainty regarding the quantities of interest. Bayesian model averaging (BMA) is one of the most popular model averaging techniques. Some studies indicate that BMA has cumbersome aspects. One of the major practical issues of using BMA is its substantial computational burden, which obstructs the process of obtaining exact estimates. A simulation method, such as Markov chain Monte Carlo (MCMC), is required to resolve this problem. Weighted‐average least squares (WALS) estimation has been proposed as an alternative to BMA. The computational burden of WALS estimation is negligible; therefore, it does not require the MCMC method. Furthermore, WALS estimation has theoretical advantages over BMA estimation. This article presents two contributions to the WALS literature. First, it applies WALS to spatial lag/error models in order to consider spatial dependence. Second, it extends WALS in order to consider explicitly the problem of multicollinearity by employing the technique of principal component regression. The small sample properties of the estimators of the proposed models are examined using Monte Carlo experiments; the results of these experiments suggest that the standard WALS may produce biased estimates when the underlying data‐generating process is a spatial lag process. Results also indicate that when the correlation among the regressors is high, the standard WALS estimators may suffer from large variances and root mean squared errors. Both of these problems are significantly mitigated by using the proposed models. Las técnicas de promediado de modelos (model averaging) vienen siendo empleadas con creciente frecuencia en las investigaciones empíricas como una alternativa a los procedimientos convencionales de selección de modelos estadísticos. Dichos procedimientos convencionales han sido criticados por no tomar en cuenta un componente clave de la incertidumbre: la incertidumbre del modelo en sí, y por lo tanto, conducen a la subestimación de la incertidumbre en la cuantificación de las valores estimados. El promediado bayesiano de modelos (Bayesian Model Averaging‐BMA) es una de las técnicas de promediado más usadas. Algunos estudios indican que BMA tiene aspectos engorrosos: uno de los principales aspectos prácticos a considerar en su uso es su pesada carga computacional, la cual obstruye el proceso de obtención de estimaciones exactas. Esta limitación hace necesario el uso de métodos de simulación, como el de la cadena de Markov de Monte Carlo (Markov Chain Monte Carlo‐MCMC). La estimación de mínimos cuadrados usando un promediado ponderado (Weighted‐Average Least Squares‐WALS) ha sido propuesta como alternativa a BMA. La carga computacional de la estimación WALS es mínima y por lo tanto no requiere del uso de MCMC. Más aun, la estimación WALS posee ventajas teóricas sobre BMA. Este artículo presenta dos contribuciones a la literatura especializada de WALS. En primer lugar, aplica WALS a modelos espaciales tipo lag/error con el fin de incorporar la dependencia espacial. En segundo lugar, modifica el método WALS, a fin de considerar explícitamente el problema de la multicolinealidad entre variables mediante el empleo de la técnica de regresión de componentes principales (Principal Component Regression‐PCR). Luego los autores utilizan experimentos Monte Carlo para examinar las propiedades de tipo “muestra pequeña” (small simple) de los estimadores de los modelos propuestos. Los resultados de los experimentos sugieren que el método WALS estándar puede producir estimaciones sesgadas cuando el proceso generador de datos subyacente (Data Generating Process‐DGP) es un proceso de retardo espacial (Spatial Lag Process‐SLP). Los resultados también indican que cuando la correlación entre las variables es alta, los estimadores estándar de WALS pueden padecer de varianzas y errores cuadráticos medios (root mean squared errors‐RMSEs) atípicamente grandes. Ambos problemas son mitigados significativamente mediante el uso de los modelos propuestos en el presente artículo. 近来,模型平均技术作为与传统模型选择流程可替换的方法,在经验调查中得到广泛应用。传统的模型选择流程忽视了模型本身的不确定性,进而低估了感兴趣样本数量的不确定性而受到批评。贝叶斯模型平均技术(BMA)是最为流行的模型平均技术之一。但已有研究表明,BMA在某些方面较为繁琐复杂,一个最主要的问题是其巨大的计算负荷阻碍模型了精确估计的过程,因此需要利用马尔可夫‐蒙特卡洛(MCMC) 之类的模拟方法进行解决。加权平均最小二乘(WALS)估计可作为BMA的可替换方法,其优点在于计算负荷可以忽略不计,因此不需要采用MCMC方法解决计算负荷问题。此外,WALS估计相比于BMA估计在理论上有一定的优势。本文针对WALS的贡献有两点:将WALS应用于空间滞后/空间误差模型以考虑空间依赖性,并利用主成分回归(PCR)拓展WALS以明确考虑多重共线性问题。本文利用蒙特卡洛实验对所提模型估计的小样本特征进行测试,结果显示当潜在数据生成过程(DGP)是一个空间滞后过程时,标准WALS可能产生有偏估计;此外,当回归量的相关性较高时,标准WALS估计量可能有较大的方差和根均方差(RMSEs).而本文提出的加权平均最小二乘估计模型能很好地缓解这两个问题。
    April 13, 2014   doi: 10.1111/gean.12032   open full text
  • Competition in Research Activity among Economic Departments: Evidence by Negative Spatial Autocorrelation.
    J. Paul Elhorst, Katarina Zigova.
    Geographical Analysis. April 13, 2014
    Despite the prevalence of both competitive forces and patterns of collaboration within academic communities, studies on research productivity generally treat universities as independent entities. By exploring the research productivity of all academic economists employed at 81 universities and 17 economic research institutes in Austria, Germany, and German‐speaking Switzerland, this study finds that a research unit's productivity negatively depends on that of neighboring research units weighted by inverse distances. This significant and exemplary robust negative relationship is compatible with the notion of competition for priority of discovery among individual researchers and the universities that employ them, and with the notion that the willingness to relocate decreases with distance. In addition, the empirical results support the hypotheses that collaboration and that the existence of economies of scale increase research productivity. A pesar de la existencia de tanto fuerzas de competencia como de patrones de colaboración dentro de la comunidad académica, los estudios sobre la productividad de investigación tratan por lo general a las universidades como entidades independientes. El presente estudio explora la productividad de investigación de todos los economistas académicos empleados en 81 universidades y 17 institutos de investigación económica en Austria, Alemania y Suiza germano‐parlante, y concluye que la productividad de una unidad de investigación depende negativamente de la productividad de las unidades vecinas ponderada por las distancias inversas que las separan. Esta relación negativa es robusta, significativa y emblemática, lo cual concuerda con el concepto de competencia por prioridad de descubrimiento (priority of discovery) entre investigadores individuales y entre las universidades que los emplean. Así mismo concuerda con la idea de que la voluntad de desplazarse disminuye con la distancia. Adicionalmente, los resultados empíricos de este estudio apoyan la hipótesis de que la colaboración y la presencia de economías de escala tienen un efecto positivo sobre (incrementan) la productividad de investigación. 尽管对学术团体的研究主要针对竞争力和合作模式,但在研究科研生产率时,大学仍被视为独立的实体。本文通过调查奥地利、德国和瑞士德语区的81所高校和17个经济研究单位经济学家的研究产出率,研究发现一个研究单位的产出效率高低负依赖于反距离加权相邻研究单位的研究产出率。这种重要且典型稳健的负相关关系与个人研究者及其所在大学关于发现优先权的竞争观念相符,同时也与研究者的调动意愿因距离增加而减少的情况相一致。此外,经验结果也支持了合作和规模经济的存在能提高研究产出率的假设。
    April 13, 2014   doi: 10.1111/gean.12031   open full text
  • An Evaluation of Small Area Population Estimation Techniques Using Open Access Ancillary Data.
    Mitchel Langford.
    Geographical Analysis. July 09, 2013
    National census data represent the “gold standard” for authoritatively portraying a country's residential population distribution, but their aggregated counts for fixed administrative areas present problems for many geographic information system (GIS) analyses. Intelligent areal interpolation algorithms assist by transferring data from one zonal system to another using ancillary data to improve accuracy. All areal interpolation methods make assumptions and generate errors, and performance varies with both specific location and the data inputs used. This study adds to our understanding of the relative merits of alternative methods by comparing dasymetric, street network, and surface‐based models interpolating across two spatial resolutions. It examines the importance of the ancillary data source used to drive the process, particularly the efficacy of open access products. Results from an empirical study show that interpolation accuracy is influenced by the choice of ancillary data input as well as the methodological approach adopted. The strongest overall performance is delivered by dasymetric mapping combined with open access data identifying the locations of buildings. Open access data sets offer considerable potential for widening the use of intelligent population interpolation tools, especially if plug‐in tools to execute these algorithms can be made available for commonly used GIS software packages. 全国人口普查数据代表了一个国家居民人口分布权威描述的黄金标准,但以固定行政区域汇总的数据进行GIS分析则存在诸多问题。智能区域插值算法利用辅助数据将数据从一个区域系统转换至另一区域系统以提高数据精度。所有的区域插值方法都作出假设并产生误差,且插值性能随着具体位置和数据输入的变化而变化。本研究对密度、街道网络、基于区域模型在两种空间分辨率下插值结果进行比较,加强对于可选插值方法优缺点的理解。它检验了应用辅助数据源,尤其是开放获取数据源对于驱动这个过程的重要性。实证结果显示,插值精度受所选择的输入辅助数据和方法的影响。总体上最好的插值效果来自于采用分区密度图并结合开放获取数据识别建筑位置。尤其是当可以常用GIS软件包中以插件工具执行这些算法时,开放数据集可为拓展智能人口插值工具应用领域提供很高的可能。
    July 09, 2013   doi: 10.1111/gean.12012   open full text
  • The Network Interpolation of Population for Flow Modeling Using Dasymetric Mapping.
    George C. Bentley, Robert G. Cromley, Carol Atkinson‐Palombo.
    Geographical Analysis. July 09, 2013
    In spatial analysis, population frequently is aggregated into source units having an areal extent. When using such data in a flow model, distances are calculated as an average between each source unit and a set of destinations. In a network, this average distance might be the shortest path between a destination and the centroid of a source unit. However, population is never concentrated at centroids nor is it uniformly distributed within each spatial unit. In urban areas, it is more likely located proximate to the road system that traverses most areal units. This article presents a method for interpolating areally aggregated data to the segments of the road network bounding an areal unit using a dasymetric approach. A case study for Phoenix, Arizona, compares using a network‐interpolated population distribution with the area‐based approach for the problem of defining service areas for a given set of facilities. A comparison of the road network used, the total demand within each service area, and the total weighted travel distance to facilities shows that the areal‐based method underestimates the portion of road network used in travel and misestimates both the expected demand of each service area and the overall travel distance to a facility. 在空间分析中,群体频繁的集聚于具有一定面积范围的源区域。当在流模型中应用这类数据时,距离计算往往采用每个源区域与目标区域之间的平均值。在一个网络中,这种平均距离可能是终点区域和源区域质心的最短路径。然而,人口并不集中于质心或均匀分布于每个空间单元中。在城市地区,人口最有可能位于横贯多数空间单元的道路系统周围。基于密度插值的方法,本文提出了将空间聚集数据插值至以道路网络为边界的区域单元。以亚利桑那州菲尼克斯进行实证分析,针对给定服务设施集合下提供服务范围的问题,比较了道路网络插值和基于区域插值方法得到的人口分布。利用道路网络的每个服务区的总需求和到服务设施的总体加权通行距离进行比较,基于区域的插值方法低估了用于通行的道路网络比例,同时也误估了每个服务区的期望需求和到一个服务设施的总体通行距离。
    July 09, 2013   doi: 10.1111/gean.12013   open full text
  • Maximum Entropy Dasymetric Modeling for Demographic Small Area Estimation.
    Stefan Leyk, Nicholas N. Nagle, Barbara P. Buttenfield.
    Geographical Analysis. July 09, 2013
    This article describes a framework for maximum entropy dasymetric modeling based on spatial allocations of public use microdata sample (PUMS) files provided by the U.S. Census Bureau. The spatial units of the PUMS (PUMAs; public use microdata areas) are too large for fine‐scale geographic analysis of populations because the common expectation is high degrees of variation within one PUMA (containing about 100,000 people). Limited demographic attribution is available at finer spatial resolutions in census summary tables for tracts and block groups. The described method (i.e., the coupling of spatial allocation procedures with dasymetric modeling) extends the literature and implements related variable associations and limiting variable constraints for allocating microdata household records to census tracts, based on sampling weights imputed using maximum entropy models. We present techniques to quantify household‐level uncertainty and to show how this information is useful for guiding the dasymetric modeling and for improving the choice of limiting and related ancillary variables. We demonstrate our methods with a PUMA in Davidson County, Tennessee. Census summary statistics are used as related variables, and land cover‐derived residential areas are included as limiting variables to refine the solution spatially to a subtract level. 本文提出了基于美国人口统计局提供的公众微观采样数据(PUMS)的空间布局分析的最大熵密度建模框架。因一个PUMA(包含10万人的统计区)通常被认为有高的变异度,导致其空间单元对于精细尺度的人口地理分析而言过大。在区块组的人口普查表中少有精细尺度的人口属性收。本文所提方法(即基于密度建模的耦合空间配置过程),基于最大熵模型对采样权重进行填充,扩展了现有方法,实现了相关变量的集成以及面向微观住户记录对空间配置的限制变量约束。我们给出了量化住户水平的不确定性量化技术,展示了该信息如何有效指导了密度建模以及增强了限制变量和相关辅助变量的选择。以西安纳西州戴维森的一个PUMA的基础统计作为相关变量,并将土地覆盖‐居民区分布作为限制变量,演示了将结果空间精细化至向亚调查块层次。
    July 09, 2013   doi: 10.1111/gean.12011   open full text
  • Estimating Missing Data Values for Georeferenced Poisson Counts.
    Daniel A. Griffith.
    Geographical Analysis. July 09, 2013
    Empirical scientists often are faced with incomplete data and desire imputations for their missing data values. The expectation–maximization algorithm is a generic tool that offers maximum likelihood solutions for such data sets. This article pursues this type of solution for Poisson random variables, utilizing a generalized linear model extension that mirrors the linear analysis of a covariance regression specification. This formulation allows a mixed model to be implemented and contrasted with a Poisson‐gamma mixture (i.e., negative binomial) model. Simple comparisons are made between model specification results for a population counts example, with and without a constraint on the total of the missing counts. 实证研究常面临数据缺失问题,需要相应的数据插补方法。最大期望算法(EM)为此类数据集进行最大似然求解提供了通用工具。本文通过提出一个可包含线性协方差回归(ANCOVA)等在内的扩展广义线性模型,将该类解决方案拓展到泊松随机变量。该方法允许建立混合模型,并可与泊松‐伽玛混合模型(即负二项式)对比。以人口计数为案例对有无缺失数据条件下模型的设定与结果进行了简要的对比。
    July 09, 2013   doi: 10.1111/gean.12015   open full text
  • A Spatially Disaggregated Areal Interpolation Model Using Light Detection and Ranging‐Derived Building Volumes.
    Harini Sridharan, Fang Qiu.
    Geographical Analysis. July 09, 2013
    Dasymetric areal interpolation is the process by which data are transferred from a spatial unit system for which they are available (source units) to another system for which they are required (target units) with the aid of ancillary information (control units). We propose a spatially disaggregated areal interpolation model for population data using light detection and ranging (LiDAR)‐derived building volumes as an ancillary variable. Innovative methods are proposed for model initialization, iterative regression and adjustment, and stopping criteria to deal effectively with control units of unequal size. The model is derived and applied at the control unit level to minimize the modifiable areal unit problem, and an iterative adjustment process is utilized to overcome the spatial heterogeneity problem encountered in earlier approaches. The use of building volume to disaggregate the population into finer scales ensures maximum correspondence with the unit at which the original population data were collected and models not only the horizontal but also the vertical population distribution. A case study for Round Rock, Texas, demonstrates that the proposed spatially disaggregated model using LiDAR‐derived building volumes outperforms earlier areal interpolation models using traditional area‐ and length‐based ancillary variables. 密度区域插值是在辅助信息的帮助下(控制元),从一个可测空间单元系统(源单元)转换至所需空间单元系统(目标元)的过程。本文提出了一个利用激光雷达(LiDAR)测量建筑物体积作为辅助变量的人口数据空间分解区域插值模型(SDAIM),一系列创新方法用于模型初始化、迭代回归和调整方法,并给出了可有效处理不等大小控制单元的停止准则。该模型从控制单元层次来最小化可变区域单元问题(MAUP),并通过迭代调整过程来克服现有方法中的空间异质性问题。利用建筑物体积将从更精细尺度进行人口空间分解,保证了原始人口数据与模型的符合度达到最大,且模型可同时反映人口的水平与垂直分布特征。以德克萨斯州的Round Rock城市为例,验证了SDAIM方法优于已有基于面积和长度辅助变量的区域插值方法。
    July 09, 2013   doi: 10.1111/gean.12010   open full text
  • Because Muncie's Densities Are Not Manhattan's: Using Geographical Weighting in the Expectation–Maximization Algorithm for Areal Interpolation.
    Jonathan P. Schroeder, David C. Van Riper.
    Geographical Analysis. July 09, 2013
    Areal interpolation transforms data for a variable of interest from a set of source zones to estimate the same variable's distribution over a set of target zones. One common practice has been to guide interpolation by using ancillary control zones that are related to the variable of interest's spatial distribution. This guidance typically involves using source zone data to estimate the density of the variable of interest within each control zone. This article introduces a novel approach to density estimation, the geographically weighted expectation–maximization (GWEM), which combines features of two previously used techniques, the expectation–maximization (EM) algorithm and geographically weighted regression. The EM algorithm provides a framework for incorporating proper constraints on data distributions, and using geographical weighting allows estimated control‐zone density ratios to vary spatially. We assess the accuracy of GWEM by applying it with land use/land cover (LULC) ancillary data to population counts from a nationwide sample of 1980 U.S. census tract pairs. We find that GWEM generally is more accurate in this setting than several previously studied methods. Because target‐density weighting (TDW)—using 1970 tract densities to guide interpolation—outperforms GWEM in many cases, we also consider two GWEM–TDW hybrid approaches and find them to improve estimates substantially. 区域插值可通过变换一组源区感兴趣变量的数据得到目标区域同一变量的分布。采用与感兴趣变量空间分布密切相关的辅助控制区来引导插值是最常用的一种方法,通常涉及采用源区域数据估计每个控制区内感兴趣变量的密度值。本文引入了地理加权最大期望算法(GWEM)来进行密度估计,综合了过去常用的最大期望算法(EM)和地理加权回归(GWR)两种技术特征。EM算法为数据分布约束的集成提供框架,地理加权则允许估计估计控制区密度比例的空间变异。以美国1980年全国普查区域的土地利用/土地覆被数据种群统计为例,对该方法的精度进行了评估。结果显示,GWEM比多种现有方法准确性更高。采用目标密度加权法(TDW)以1970年束密度来进行插值在许多情况下优于GWEM,融合GWEM‐TDW两种方法可大幅改善估计结果。
    July 09, 2013   doi: 10.1111/gean.12014   open full text