Adaptive Cell Tower Location Using Geostatistics. 基于地统计学的适应性基站发射塔选址研究

In this article, we address the problem of allocating an additional cell tower (or a set of towers) to an existing cellular network, maximizing the call completion probability. Our approach is derived from the adaptive spatial sampling problem using kriging, capitalizing on spatial correlation between cell phone signal strength data points and accounting for terrain morphology. Cell phone demand is reflected by population counts in the form of weights. The objective function, which is the weighted call completion probability, is highly nonlinear and complex (nondifferentiable and discontinuous). Sequential and simultaneous discrete optimization techniques are presented, and heuristics such as simulated annealing and Nelder–Mead are suggested to solve our problem. The adaptive spatial sampling problem is defined and related to the additional facility location problem. The approach is illustrated using data on cell phone call completion probability in a rural region of Erie County in western New York, and accounts for terrain variation using a line‐of‐sight approach. Finally, the computational results of sequential and simultaneous approaches are compared. Our model is also applicable to other facility location problems that aim to minimize the uncertainty associated with a customer visiting a new facility that has been added to an existing set of facilities.     

Local Forms of Spatial Analysis

Local forms of spatial analysis focus on exceptions to the general trends represented by more traditional global forms of spatial analysis. There is currently a rapid expansion in the development of such techniques but their history almost exactly parallels that of Geographical Analysis, with the first examples of local analysis appearing in the late 1960s. Indeed, Geographical Analysis has published many of the significant contributions in this field. This paper reviews the development of local forms of spatial analysis and assesses the current situation. Following a discussion on the nature and importance of local analysis, examples are given of local forms of point pattern analysis; local graphical approaches; local measures of spatial dependency; the spatial expansion method; adaptive filtering; multilevel modeling; geographically weighted regression; random coefficients models; autoregressive models; and local forms of spatial interaction models.     

Geographically Weighted Extreme Learning Machine: A Method for Space–Time Prediction

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.     

PERSISTENCE OF REGIONAL UNEMPLOYMENT: APPLICATION OF A SPATIAL FILTERING APPROACH TO LOCAL LABOR MARKETS IN GERMANY*

ABSTRACT The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analyzing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region‐specific information (e.g., the endowment of natural resources, or the size of the “home market”) that is usually incorporated in the fixed effects coefficients. The advantages of our proposed procedure are that the spatial filter, by incorporating region‐specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time‐stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive coefficients estimated for unemployment data for German NUTS‐3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.     

基于GWR模型的合肥居住地价影响因素研究

以合肥市主城区为例,基于2010-2014年居住用地的出让数据,运用地统计法、GWR模型等方法,对合肥市居住地价的空间异质性及其影响因素进行研究。研究表明：①合肥市居住地价的空间分布呈现出显著的多中心的空间结构,地价的峰值区分别以老城区、政务区天鹅湖及滨湖新区塘西河公园为中心呈现圈层式分布;②不同的地价影响因素表现出不同的空间分布特征,其中容积率对居住地价的贡献度空间差异最大,其次是宗地面积,主干路次之,交通站点对居住地价的贡献度最小;③厘清各影响因素对地价的作用机制,建立动态的数字地价模型,不仅能促进土地资源的集约利用,重塑城市的空间结构,而且能为城市整体价值的发挥提供重要的理论支撑。     

A TRANSLOG APPROACH TO CONSUMER SPATIAL BEHAVIOR*

With the view that travel behavior stems from the principle of utility maximization, in this paper I present a spatial translog demand model that accounts for interdependence among travel alternatives and that handles varying elasticities of substitution for various destination pairs. Using simulation as the mode of inquiry, this model describes the effect of spatial size, spatial configuration, and spatial substitution on spatial interaction. In addition to indicating how varying spatial sizes and configurations affect the average trip length and the trip making pattern, the simulation results also point out the possible effect of having spatially dependent locations in the system. Competing destinations increase the attractiveness of nearby locations, and complementary destinations reduce the impeding effect of space. The model is primarily relevant to the demand for shopping trips.     

Geographically Weighted Cox Regression for Prostate Cancer Survival Data in Louisiana

The Cox proportional hazard model is one of the most popular tools in analyzing time-to-event data in public health studies. When outcomes observed in clinical data from different regions yield a varying pattern correlated with location, it is often of great interest to investigate spatially varying effects of covariates. In this paper, we propose a geographically weighted Cox regression model for sparse spatial survival data. In addition, a stochastic neighborhood weighting scheme is introduced at the county level. Theoretical properties of the proposed geographically weighted estimators are examined in detail. A model selection scheme based on the Takeuchi’s model robust information criteria is discussed. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze real data on prostate cancer from the Surveillance, Epidemiology, and End Results cancer registry for the state of Louisiana.     

基于DBSCAN算法的民宿集群识别、分布格局及影响因素——以南京市为例

以南京市Airbnb网站民宿商家为例,综合利用DBSCAN聚类算法、空间变异函数、地理加权回归模型刻画南京市民宿的空间特征及影响因素.结果表明:①南京市民宿已经形成"25124"的5等级14集群格局,呈现"两横两纵"的"井"字形空间布局特征;②民宿密度分布存在空间异质性,各个街道之间存在较强的空间自相关,民宿密度分布受...     

Using Spatial Filters and Exploratory Data Analysis to Enhance Regression Models of Spatial Data

Residual spatial autocorrelation is a situation frequently encountered in regression analysis of spatial data. The statistical problems arising due to this phenomenon are well‐understood. Original developments in the field of statistical analysis of spatial data were meant to detect spatial pattern, in order to assess whether corrective measures were required. An early development was the use of residual autocorrelation as an exploratory tool to improve regression analysis of spatial data. In this note, we propose the use of spatial filtering and exploratory data analysis as a way to identify omitted but potentially relevant independent variables. We use an example of blood donation patterns in Toronto, Canada, to demonstrate the proposed approach. In particular, we show how an initial filter used to rectify autocorrelation problems can be progressively replaced by substantive variables. In the present case, the variables so retrieved reveal the impact of urban form, travel habits, and demographic and socio‐economic attributes on donation rates. The approach is particularly appealing for model formulations that do not easily accommodate positive spatial autocorrelation, but should be of interest as well for the case of continuous variables in linear regression.     

北京房地产中介空间格局演化特征及驱动因素分析

本文基于微观企业数据,采用泰森多边形、核密度及空间自相关方法,多尺度考察2001-2010年北京房地产中介门店空间格局演化特征,通过构建地理加权回归模型（GWR）,探究2010年房地产中介空间格局驱动因素。研究表明：①房地产中介集中分布在城六区,数量增速最快功能区是城市发展新区。②空间格局不均衡性增大,空间集聚态势加强。③集聚主核心点出现由"东城"向"朝阳"方向的东推位移,集聚程度加深,主核心区团块面积扩大,次级核心点增多,"两轴、两带、多中心"基本版图形成。④空间集聚现象明显,热点区域扩张,但始终集中在区域中心。⑤驱动效应最大的因素为常住人口,多种驱动因素效应均比较强的街区（乡镇）基本上属于各区县经济水平相对发达的区域。此外,政府政策对其影响也较大。     

Redundancy as an Index of Change in Point Pattern Analysis

Point pattern analysis based on concepts from information theory can go beyond existing techniques. Direct measurement of spatial form is achieved when Thiessen polygons are constructed around the points; in this scheme, each point's proportion of total area may be treated like a probability. Three information-theoretic indices are available for analysis of a distribution of such probabilities. Entropy is density dependent. Redundancy, defined as the difference between observed and maximum entropy, seems to avoid this problem when the number of individuals in a pattern exceeds twenty. Comparisons of prior and posterior redundancy provide an indication of change in overall pattern form. An information gain expectation reflects changes for each individual in a pattern. Here, point-area redundancy parameters are determined for Poisson-generated patterns, using a gamma distribution of polygon areas and a computer-generated set. An application to an urban crime problem illustrates the use of these parameters in the analysis of pattern change.     

Local indicators for categorical data: impacts of scaling decisions

When the geographic distribution of landscape pattern varies, global indices fail to capture the spatial nonstationarity within the dataset. Methods that measure landscape pattern at a spatially local scale are advantageous, as an index is computed at each point in the dataset. The geographic distribution of local indices is used to discover spatial trends. Local indicators for categorical data (LICD) can be used to statistically quantify local spatial patterns in binary geographic datasets. LICD, like other spatially local methods, are impacted by decisions relating to the spatial scale of the data, such as the data grain (p), and analysis parameters such as the size of the local neighbourhood (m). The goal of this article is to demonstrate how the choice of the m and p parameters impacts LICD analysis. We also briefly discuss the impacts spatial extent can have on analysis; specifically the local composition measure. An example using 2006 forest cover data for a region in British Columbia, Canada where mountain pine beetle mitigation and salvage harvesting has occurred is used to show the impacts of changing m and p. Selection of local window size (m = 3,5,7) impacts the prevalence and interpretation of significant results. Increasing data grain (p) had varying effects on significant LICD results. When implementing LICD the choice of m and p impacts results. Exploring multiple combinations of m and p will provide insight into selection of ideal parameters for analysis.     

兰新高铁对西北地区可达性及经济联系的影响

高铁正在深刻改变和影响我国区域空间格局，兰新高铁作为丝绸之路经济带上重要交通线，对西北地区经济发展具有重要推动作用。通过GIS网络分析及成本加权栅格法构建模型，综合选用最短时间距离、加权平均旅行时间及经济联系强度指标，定量分析兰新高铁对西北地区可达性及经济联系的影响。结果发现：兰新高铁引发了非均衡的时空收敛效应，影响和重塑了区域空间结构，沿线城市可达性提升优于非沿线城市，“核心-边缘”格局得到强化；兰新高铁提升了中心城市的辐射能力，加速中心城市的空间扩张和重构，中心城市一日交流圈面积（3h）均有增加，在空间上沿高铁线呈带状分布；兰新高铁增强了城市间经济联系，促进高铁沿线经济带的发展，沿线城市经济联系中心度得到提高，区域经济联系模式“廊道效应”明显。本研究为完善西北地区交通网络及推动丝绸之路经济带建设提供参考。     

Ecological Inference and Spatial Variation of Individual Behavior: National Divide and Elections in Catalonia

When in geography one reconstructs individual behavior starting from aggregated data through ecological inference, a crucial aspect is the spatial variation of individual behavior. Basic ecological inference methods treat areas as if they were all exchangeable, which in geographical applications is questionable due to the existence of contextual effects that relate to area location and induce spatial dependence. Here that assumption is avoided by basing ecological inference on a model that simultaneously does a cluster analysis, grouping together areas with similar individual behavior, and an ecological inference analysis in each cluster, estimating the individual behavior in the areas of each group. That allows one to capture most of the spatial dependence and summarize the individual behavior at a local level through the behavior estimated for each cluster. This approach is used to investigate vote switching in Catalonia, where voters split across a national allegiance divide on top of the ideological divide. That leads to Catalans having a lot of options to choose from, and to them voting differently depending on whether the election is for the Catalan parliament or for the Spanish parliament. To investigate that, the results in the two most recent pairs of such elections are analyzed by simultaneously clustering areas based on the similarity of their vote and vote switch patterns, and estimating one vote switch pattern for each cluster. As a result, Catalonia is partitioned into four clusters that have a strong spatial structure, with all the areas in the same cluster having similar demographic composition. The estimated vote switch patterns are quite different across clusters but very similar across pairs of elections, and they help assess how the differential voter turnout and the strategic dual vote effects vary in space.     

COVID-19 in Chihuahua,Mexico: Assessing its spatial behaviour through the inverse distance weighted interpolation technique

This study focused on the spatial evolution of COVID-19 in the state of Chihuahua, Mexico. Data were retrieved from governmental databases and analyzed by means of GIS, applying the inverse distance weighted (IDW) method. The period of December 2019 through November 2021 was split into eight seasons. The root mean square error (RMSE) was used to assess the reliability of the interpolations, showing acceptable values (RSME < 25). During the period, the municipalities of Juarez and Chihuahua reached the highest number of confirmed cases and deaths, Juarez being the main hotspot of contagion (37.2% of confirmed cases; 46.9% of deaths). Four waves of contagion were identified during the evaluated period, with Fall 2020 being the strongest season. Since Fall 2020, the spread of the disease was more often observed in municipalities with the highest human mobility. Although the spread of COVID-19 decreased after Spring 2021, in Fall 2021 records indicated a continuous increase in cases in the state. That could be due to a relaxation of the implementation of sanitary measures, as well as to the propagation of novel COVID-19 variants having an elevated infectious level. Geospatial techniques allowed for an understanding of the spatial spread of COVID-19 and could be useful for its control.     

‘Land of Fires’: Urban Growth,Economic Crisis,and Forest Fires in Attica,Greece

This study investigates the spatial distribution and basic characteristics of 2692 forest fires between 2000 and 2011 in Attica, Greece, a rapidly expanding urban region. Fire variables were assessed, together with variables describing the socio‐economic local context in three distinct periods of Athens’ expansion (2000–2003: semi‐compact expansion and economic growth before the 2004 Olympic Games; 2004–2007: infrastructure development and discontinuous expansion following the Olympic Games; 2008–2011: crisis‐driven decline of the construction industry). Fire characteristics changed over the three periods following the negative trends in the construction industry. Burnt areas decreased in the rural area around Athens and the opposite pattern was observed in the peri‐urban belt. Our findings suggest that the temporal and spatial distribution of forest fires in Attica reflects the construction boom stimulated by the 2004 Olympic Games and the subsequent economic crisis.     

Geographical and Temporal Weighted Regression (GTWR)

Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19‐year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling.     

Markovian Models of Movement Within Urban Spatial Structures

Much work has assumed that movement within urban spatial structures is an “adaptive” process. Nonetheless, mathematical models have not yet been specified and tested which formulate both how different individuals “adapt” over time in destination or route selection, and how predictions about aggregate movement can be derived from postulates about different persons. Two adaptive first-order Markov models for heterogeneous individuals are suggested by the literature. When formulated and tested, however, these models are inadequate to describe travel within urban spatial structures. This implies that the use of Markovian processes to model movement may be overrated. More confidence may be placed in other formulations such as linear learning models of route and destination choice.     

基于可达性与场强模型的中国地级以上城市空间场能测度

空间场能是区域中心城市借助区域联系通道带动腹地发展而产生的"势能差"的抽象表达,可有效刻画区域发展格局与空间差异。采用主成分分析法与指标体系计算城市结节性指数,借助ArcGIS软件平台,采用累积耗费距离法、K阶数据场和ESDA等方法,综合测度了中国287个地级以上城市可达性与空间场能,并分别从省域、市域、县域等单元探讨了中国东中西三大地带与南北场能差异的空间格局与演变特征。结果表明:中国地级以上城市的可达性条件不断改善,平均可达性时间从1991年的246.98min缩短为2010年的193.43min。空间场能显著增长且空间分异性显著,分布结构由单核向多极网络状形态转变。县域场能存在显著的空间极化现象,场能热点区域的分布呈现显著的热点-次热点-次冷点-冷点自东向西带状分布的格局。依据场能分析结果,将全国划分为3个一级经济区和26个二级城市经济区。     

Symptoms of Instability in Models of Spatial Dependence

In recent years, there has been a growing interest in the problems caused by the existence of instability in cross-sectional regressions. The results about local autocorrelation measures are part of this debate, as are the proposals concerning the concept of geographically weighted regressions. This article also deals with the problem of stability (or the lack thereof), but focusing the discussion on the supposition of constancy in the parameter of spatial dependence. In most cases, this assumption is treated, with the risks that this involves, as a maintained hypothesis, which should be ascertained before continuing with the modeling exercise. In the article, we present a simple heterogeneity test for this type of parameters, based on the Lagrange Multiplier principle. To illustrate its use, we take the distribution of per capita income among the European regions as our discussion case. According to our results, there are clear signs of structural breaks in the spatial distribution of this variable and the scale factor and the autocorrelation coefficient appear to be principal actors.