Spatial Decomposition of Segregation Indices: A Framework Toward Measuring Segregation at Multiple Levels

Segregation measures based upon data gathered at different geographical levels cannot provide consistent results because of the scale effect under the Modifiable Areal Unit Problem (MAUP) umbrella. This paper proposes a framework, which decomposes segregation attributable to different geographical levels, to conceptually link segregation values obtained from multiple geographical levels together such that differences in segregation values among levels are accounted for. Using two different indices, the dissimilarity index D and the diversity index H, this paper illustrates the decomposition methods specific to these indices. When these indices are decomposed, local measures of segregation pertaining to multiple geographical levels are computed. These local segregation measures indicate the levels of segregation contributed by the local units and the regional units to the entire study area.     

Scales of regional income disparities in the USA, 1955 2003

This article examines multiple dimensions of regional per capitaincome disparities in the USA between 1955 and 2003 with a particularfocus on scalar effects. It combines various exploratory analyticaltools of spatial disparities, including inequality indices,mobility indices, kernel density estimation, spatial autocorrelationstatistics and scale variances, to analyse regional averageper capita income distributions at multiple spatial scales,ranging from counties to multi-state regions. The analysis revealspreviously unrecognised systematic patterns of cross-scalardynamics, whereby spatial income disparities are increasinglymore pronounced at smaller scales in the last few decades.     

Neural Network Modeling of Constrained Spatial Interaction Flows: Design, Estimation, and Performance Issues

In this paper a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedure for parameter estimation, such as the Alopex procedure. A benchmark comparison against the standard origin constrained gravity model and the two–stage neural network approach, suggested by Openshaw (1998), illustrates the superiority of the proposed model in terms of the generalization performance measured by ARV and SRMSE.     

Gender, space, and the location changes of jobs and people: a spatial simultaneous equations analysis

This article summarizes a spatial econometric analysis of local population and employment growth in the Netherlands, with specific reference to impacts of gender and space. The simultaneous equations model used distinguishes between population- and gender-specific employment groups, and includes autoregressive and cross-regressive spatial lags to detect relations both within and among these groups. Spatial weights matrices reflecting different bands of travel times are used to calculate the spatial lags and to gauge the spatial nature of these relations. The empirical results show that although population–employment interaction is more localized for women's employment, no gender difference exists in the direction of interaction. Employment growth for both men and women is more influenced by population growth than vice versa. The interaction within employment groups is even more important than population growth. Women's, and especially men's, local employment growth mostly benefits from the same employment growth in neighboring locations. Finally, interaction between these groups is practically absent, although men's employment growth may have a negative impact on women's employment growth within small geographic areas. In summary, the results confirm the crucial roles of gender and space, and offer important insights into possible relations within and among subgroups of jobs and people.     

环渤海沿海地市低碳转型绩效评价与影响因素分析

基于低碳转型的内涵,本文以环渤海17个沿海地市2005-2012 年的面板数据为基础,构建环渤海沿海地市低碳转型的评价指标体系,运用集对分析法对其进行评价研究,并采用核密度估计模型及GIS 空间分析技术对其结果进行时空差异分析,揭示其内在变化规律。研究结果表明：①自2005 年到2012年,环渤海沿海地市低碳发展颓势明显,各地市低碳转型没有取得明显进展,各地市之间转型绩效的整体差距较大。②环渤海沿海地市经济增长在空间格局中并没有显著的变化,而在产业结构、能源利用、生态环境、发展支撑方面均呈现出反向变化的态势,说明低碳转型成效并不显著,仍然保持传统的发展态势。③环渤海沿海地市低碳转型绩效的时空差异是多种因素综合作用的结果,包括资金、人力与基础设施等经济社会支撑因素,国家政策、科学技术、低碳意识及企业家素质等新型推动因素,资源（能源）禀赋、区位条件等自然基础因素。     

A SPATIO‐TEMPORAL KERNEL METHOD FOR MAPPING CHANGES IN PREHISTORIC LAND‐USE PATTERNS

Archaeologists are accustomed to considering both the spatial distributions of sites and the temporal distributions of dates as means of analysing the dynamics of prehistoric societies. However, spatial and temporal approaches have thus far remained largely separate, rather than being combined within a single, unified framework. A formal methodology is outlined that combines univariate kernel density estimation based on radiocarbon dates with bivariate kernel density estimation based on spatial site coordinates; the approach allows archaeologists to arrive at reconstructed land‐use distributions through time that not only correct for the problematic issue of site contemporaneity, but also reflect the continuous nature of the archaeological record. The model is implemented using as a data set a series of sites from the Mesolithic of Atlantic Iberia; the results demonstrate that it is capable of providing key insights into changing patterns of land use that are not apparent from either the temporal or the spatial perspective alone.     

Measuring the Scale of Segregation Using k‐Nearest Neighbor Aggregates

Nearly all segregation measures use some form of administrative unit (usually tracts in the United States) as the base for the calculation of segregation indices, and most of the commonly used measures are aspatial. The spatial measures that have been proposed are often not easily computed, although there have been significant advances in the past decade. We provide a measure that is individually based (either persons or very small administrative units) and a technique for constructing neighborhoods that does not require administrative units. We show that the spatial distribution of different population groups within an urban area can be efficiently analyzed with segregation measures that use population count‐based definitions of neighborhood scale. We provide a variant of a k‐nearest neighbor approach and a statistic spatial isolation and a methodology (EquiPop) to map, graph, and evaluate the likelihood of individuals meeting other similar race individuals or of meeting individuals of a different ethnicity. The usefulness of this approach is demonstrated in an application of the method to data for Los Angeles and three metropolitan areas in Sweden. This comparative approach is important as we wish to show how the technique can be used across different cultural contexts. The analysis shows how the scale (very small neighborhoods, larger communities, or cities) influences the segregation outcomes. Even if microscale segregation is strong, there may still be much more mixing at macroscales.     

广州市外来人口居住隔离及影响因素研究

利用广州市人口普查街道(部分居委会)数据总结1990-2000年外来人口居住隔离的时空特征:隔离程度呈增加趋势,核心和外围区隔离程度及变化差异大,散点状为主的空间模式,并呈现异质性和非稳定性的社会特征。通过典型社区问卷访谈,对比外来人口与户籍低收入人口,建构影响因素的逻辑回归(Logistic regression)模型,显示户籍的属地差别是产生隔离的根本原因,与户籍属地管理相关的劳动力市场分割和住房政策共同构成了排斥的制度环境,限制了外来人口的住房选择,把其推向低端私人租房市场。在有限的居住选择下,交通和经济等个体择居要素影响了隔离空间的分布和社会特征,以城中村为代表低端私人租房来源的布局特征决定并固化了隔离空间的分布模式。     

基于模糊关系识别的多要素空间离散化方法——以江苏阜宁人口与经济分析为例

社会、经济要素的空间离散化是精细化县级主体功能区划的重要需求,本文提出了一种基于模糊关系识别的空间数据离散化方法。该方法利用广义权距离实现对专家知识与多系统分层要素的综合集成,并通过建立待离散化要素与其影响指标的模糊关系识别模型,获得空间离散化权重。以2009年江苏省阜宁县人口、GDP以及经济发展水平的空间离散化为例进行实例分析。结果显示,本文提出的空间离散化方法具有较好的准确性与可信度,可较好揭示各影响要素对待离散要素的空间影响。     

A Methodology for Neural Spatial Interaction Modeling

This paper attempts to develop a mathematically rigid and unified framework for neural spatial interaction modeling. Families of classical neural network models, but also less classical ones such as product unit neural network ones are considered for the cases of unconstrained and singly constrained spatial interaction flows. Current practice appears to suffer from least squares and normality assumptions that ignore the true integer nature of the flows and approximate a discrete‐valued process by an almost certainly misrepresentative continuous distribution. To overcome this deficiency we suggest a more suitable estimation approach, maximum likelihood estimation under more realistic distributional assumptions of Poisson processes, and utilize a global search procedure, called Alopex, to solve the maximum likelihood estimation problem. To identify the transition from underfitting to overfitting we split the data into training, internal validation, and test sets. The bootstrapping pairs approach with replacement is adopted to combine the purity of data splitting with the power of a resampling procedure to overcome the generally neglected issue of fixed data splitting and the problem of scarce data. In addition, the approach has power to provide a better statistical picture of the prediction variability. Finally, a benchmark comparison against the classical gravity models illustrates the superiority of both, the unconstrained and the origin constrained neural network model versions in terms of generalization performance measured by Kullback and Leibler's information criterion.     

On Geocomputational Determinants of Entropic Variations for Urban Dynamics Studies

Monitoring population characteristics and their patterns of spatial evolution are fundamental components for urban management and policy decision‐making. Societal issues such as health, transport, or crime are often explored using a range of models describing the urban dynamics of population attributes at specific scales that can be seen as complementary. Using and simulating data at different scales of aggregation asks for the need to analyze and compare spatiotemporal variations in order to better understand the model behaviors and emerging properties of the geosimulation. This article analyzes the uses of the entropy measure in the literature and constraining factors needed for its potential extension to explore the variations in geographic and time scales. In particular, the article discusses the need for a truly spatial entropy that takes into account the spatial contiguities of the observations usually aggregated within a zoning system of areal units. Two generic solutions are exposed for the various geometries and attribute structures used for census‐related analyses; they are based on existing measures for point data using (i) co‐occurrences of observations and (ii) discriminant ratios of distances between groups of observations. Their extensions to areal compositional data are articulated around their conceptual changes and geocomputational challenges. A revisited and new version of the entropy decomposition theorem, encompassing a spatiality concept semantically related to correlation, is also presented as efficiently reusing the constrained hierarchical zoning system of administrative units to enable discovery of emerging spatial pattern features from the geosimulation. A comparison of the results between the classical use of entropy and the spatial entropy framework devised shows the flexibility and added capabilities of the approach for new types of analyses, thus allowing new insight into studies of population dynamics.     

基于区域经济差异视角的重庆市经济区划研究

本文在传统区域经济差异指标泰尔指数的基础上提出经济区划度的概念,对重庆市自直辖以来出台的不同经济区划方案进行评价;并运用GIS热点分析方法,探讨经济区划调整的可能性。研究结果表明：①历次政府区划方案中,“五大功能区”经济区划度远高于其余三种方案,但近年来经济区划度呈现出逐年下降趋势,并不稳定;②基于GIS热点分析得到三种初始区划方案中,方案I（“四圈+两翼”）的经济区划效果表现最佳,并显著优于“五大功能区”经济区划,且其效果呈现出逐年上升的趋势。③综合考虑政府规划决策、地形地貌、历史发展基础以及交通条件等方面因素,微调后的方案I的区划效果良好且更符合实际区划要求。研究对重庆及西部内陆地区的可持续发展具有一定科学指导意义。     

A Statistical Method for the Detection of Geographic Clustering

Kernel‐based, smoothed estimates of spatial variables are useful in exploratory analyses because they yield a clear visual image of geographic variability in the underlying variable. In this paper I suggest an approach for assessing the significance of peaks in the surface that result from the application of the smoothing kernel. The approach may also be thought of as a method for assessing the maximum among a set of suitably defined local statistics. Local statistics for data on a regular grid of cells are first defined by using a Gaussian kernel. Results from integral geometry are then used to find the probability that the maximum local statistic (M) exceeds a given critical value (M). Approximations are provided that make implementation of the approach straightforward. Future work will address several other issues associated with local statistics that have been defined in this way, including edge effects, and the effects of global spatial autocorrelation on the choice of critical value.     

Distance and social interaction in a Victorian city

The paper examines patterns of marriage in a small industrial city—Huddersfield—between 1850 and 1880, when the residential structure of the city was becoming more modern in its spatial organization. The need to interpret distance-decay patterns of interaction in their social context is stressed. The apparently unchanging relationship between physical distance and frequency of interaction is related to the balance between changing patterns of individual mobility, class consciousness and scales of residential segregation. The more extensive interaction fields of the rich are attributed to their greater mobility, but also their lower population density. The close-knit patterns of the poor reflect their higher population density and more particularly the segregation of the Irish community. Finally, the differences between normative (within-class) and non-normative (between-class) marriage distances are considered. It is suggested that physical distance operated independently of social distance, although this conclusion requires further testing at different scales of analysis, and using information on forms of interaction other than marriage.     

基于POI大数据的资源型城市功能区识别方法与实证——以辽宁省本溪市为例

精准识别城市功能区对于优化协调城市人地系统，完善城市规划及促进城市可持续发展具有重要意义。选取公众认知度和地物一般面积对行业POI数据核密度进行加权，构建城市功能区定量识别方法，以本溪为例，输出城市功能区分布图并开展分析。结果表明：①考虑公众认识度与一般面积的核密度加权方法具有更高的实际性和准确性；②本溪市中心城区受地块大小、交通等因素影响，形成“多中心”组团、“单一-混合”功能区协同发展的空间格局。应进一步完善本溪新城区公共服务体系、优化旅游业产业链条，推进资源型城市转型进程。     

乡村振兴导向下乡村发展能力的多维测度及时空交互特征

乡村振兴要求走乡村内生性发展道路,客观认知乡村发展能力的时空分异格局和交互关系对乡村振兴战略实施具有重要意义。本文从农业产业发展、农村基础建设和农民社会生活三个维度构建测度指标体系,并引入面板向量自回归模型识别影响乡村发展能力的敏感因素,以此测度乡村发展能力指数,进而采用探索性时空数据分析方法揭示乡村发展能力的时空交互特征。结果表明：(1)我国中部地区多维乡村发展能力提升,但呈明显的空间异质性,不同维度间差异化显著;(2)中部地区多维乡村发展能力局域空间格局呈现较强的动态性并整体表现出较强的空间依赖关系;(3)邻接地级市多维乡村发展能力的时空交互网络以正向关联为主,局部空间呈差异化的竞合态势。     

基于空间特征的秦巴山区农村居民点整理分区与潜力测算

整理分区划定和整理潜力测算是农村居民点整理的核心内容。本文综合地形位、缓冲区、可达性、景观指数和Voronoi 图,引入空间分异特征指数,定量分析农村居民点用地特征,实现整理区合理划分;改进潜力测算模型,结合经济社会、土地利用、农户意愿因素,构建整理潜力综合评价指标体系,以秦巴山区竹溪县为例进行实证研究。结果表明：竹溪县农村居民点空间分异特征指数值位于0.0433-0.8038 之间;全县农村居民点可分为优先整理区、重点整理区、优化调控区和城镇发展区,现实潜力904.63 hm²,仅为传统潜力的65.42%;根据各乡镇潜力系数,划分为3 级潜力区。空间分异的潜力测算、整理区和潜力区划分,为确定因地制宜的整理模式和符合实际的整理时序提供依据。     

空间联系视角的沈阳市多中心城市结构研究

以沈阳市为研究案例,借鉴国际著名团队POLYNET的研究经验并加以改良,进行了空间联系视角的多中心城市结构的实证分析。分别从人口密度分布、就业-居住空间联系网络与企业空间组织网络三个维度来识别沈阳市的人口中心、就业中心与产业中心,综合分析这些"中心"得出四点结论:①城市核心区就业与居住中心构成十字交叉的内循环结构;②浑南新区次中心特征颇具雏形;③外围人口中心、就业中心与产业中心错位发展;④沈阳市呈现不均衡的多中心结构特征。     

浙江省区域创新中心空间格局及其驱动机制研究

区域创新中心建设对区域创新资源整合优化,发挥区域创新优势意义重大。研究运用最邻近距离、核密度估计、探索性空间分析等方法,分析浙江省区域创新中心空间分布、聚类特征及影响因素,得出以下结论:2013年浙江省区域创新中心空间分布整体呈现"周边少,中心多"的态势,空间点分布呈现凝聚特征,空间核分布存在明显不均衡现象;各县市区域创新中心空间分布同样呈现集聚特征,且存在明显的高值与低值集聚;区域创新中心的建设与发展需要市场各级主体的共同合作,同时还受到区域创新能力、经济发展水平、产业集群优势、政府政策因素以及空间邻近效应的影响。