Local Indicators of Network-Constrained Clusters in Spatial Point Patterns

The detection of clustering in a spatial phenomenon of interest is an important issue in spatial pattern analysis. While traditional methods mostly rely on the planar space assumption, many spatial phenomena defy the logic of this assumption. For instance, certain spatial phenomena related to human activities are inherently constrained by a transportation network because of our strong dependence on the transportation system. This article thus introduces an exploratory spatial data analysis method named l ocal i ndicators of n etwork-constrained c luster s (LINCS), for detecting local-scale clustering in a spatial phenomenon that is constrained by a network space. The LINCS method presented here applies to a set of point events distributed over the network space. It is based on the network K -function, which is designed to determine whether an event distribution has a significant clustering tendency with respect to the network space. First, an incremental K -function is developed so as to identify cluster size more explicitly than the original K -function does. Second, to enable identification of cluster locations, a local K -function is derived by decomposing and modifying the original network K -function. The local K -function LINCS, which is referred to as KLINCS, is tested on the distribution of 1997 highway vehicle crashes in the Buffalo, NY area. Also discussed is an adjustment of the KLINCS method for the nonuniformity of the population at risk over the network. As traffic volume can be seen as a surrogate of the population exposed to a risk of vehicle crashes, the spatial distribution of vehicle crashes is examined in relation to that of traffic volumes on the network. The results of the KLINCS analysis are validated through a comparison with priority investigation locations (PILs) designated by the New York State Department of Transportation.     

Spatial Cluster Detection in Spatial Flow Data

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.     

Duration Modeling of Spatial Point Patterns

This paper attempts to further the research by Odland and Ellis (1992) in applying event history methodology to the analysis of spatial point patterns (that is, event patterns). Its empirical focus is the event pattern derived from the adoption of an agricultural innovation, the Harvestore, in southern Ontario, Canada, from 1963 to 1986. Event history analysis involves the use of discrete-state, continuous-time stochastic models to investigate a temporal longitudinal record on discrete variables. Event history models are usually concerned with durations of time between events and the effects of intertemporal time dependencies on future event occurrences. As such, they are often referred to as duration models. Many of the methods used in event history analysis allow the use of other nonnegative interval measurements in place of standard temporal intervals to investigate a series of events. In particular, spatial intervals (or durations) of distances between events may also be accommodated by event history models. Our analysis extends the previous research of Odland and Ellis (1992) by using a wider range of parametric models to explore duration dependence, investigating the role of spatial censoring, and using a more extensive set of explanatory variables. In addition, simulation experiments and graphical tests are used to evaluate the empirical event pattern against one generated from Complete Spatial Randomness. Results indicate that the event pattern formed by the Harvestore adopter farms is clustered (that is, is described by positive duration dependency), the sales agent is a significant factor in the distribution of adopters, and that contrasting results are obtained from the analysis using censored data versus uncensored data.     

Population size, concentration, and civil war. A geographically disaggregated analysis

Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses put forward in the conflict literature regarding the relationship between the size and location of population groups. These hypotheses are based on population mass and concentrations, distances, and some residual state-level characteristics. The hypotheses are tested with a new dataset – ACLED (Armed Conflict Location and Events Dataset) – that disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze these types of data. The analysis shows that the risk of conflict events increases with local population size, and is particularly large in highly populous locations within a country. We find only a moderate difference in conflict-proneness between areas located close to or far from a country's capital, except when combined with population concentration: Conflict events cluster particularly strongly in larger population concentrations that are distant from the capital, such as the Eastern provinces of the Democratic Republic of Congo.     

Accuracy of Count Data Estimated by the Point‐in‐Polygon Method

This paper analyzes the accuracy of count data estimated by the point‐in‐polygon method. A point‐in‐polygon interpolation model is proposed, based on a stochastic distribution of points and the target zone, in order to represent a variety of situations. The accuracy of estimates is numerically investigated in relation to the size of the target zone and the distribution of points, and the optimal location of representative points is discussed. The major findings of this paper are as follows: (1) though the relative accuracy of estimates generally increases monotonously with the size of the target zone, the monotoneity is often disturbed by the periodicity in the spatial configuration of source zones and the point distribution; (2) the point‐in‐polygon and the areal weighting interpolation methods have the same accuracy of estimates when points are concentrated in less than 12–15 percent area around the representative point in source zones; (3) the point‐in‐polygon method is not so robust against the locational gap between points and the representative point; (4) the optimal location of representative points is given by the spatial median of points.     

Multi‐Type,Multi‐Zone Facility Location

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.     

EXTENDING A SMOOTH PARAMETER MODEL TO FIRM LOCATION ANALYSES: THE CASE OF NATURAL GAS ESTABLISHMENTS IN THE UNITED STATES

This paper extends recent developments in regional growth modeling that use spatial regime switching functions to a count regression model of firm location events. The smooth parameter count model (SPCM) allows for a parsimonious parameterization of locally varying coefficients while simultaneously attending to excess‐zero count events. An empirical application examines natural gas establishment growth between 2005 and 2010. The smooth parameter model appears to outperform a standard zero‐inflated count model. The SPCM may be extended to the location analysis of other industries with the identification of transition variables related to the supply or demand oriented cost structure of the sector.     

Nonparametric Significance Test for Discovery of Network‐Constrained Spatial Co‐Location Patterns

Spatial co‐location patterns are useful for understanding positive spatial interactions among different geographical phenomena. Existing methods for detecting spatial co‐location patterns are mostly developed based on planar space assumption; however, geographical phenomena related to human activities are strongly constrained by road networks. Although these methods can be simply modified to consider the constraints of networks by using the network distance or network partitioning scheme, user‐specified parameters or priori assumptions for determining prevalent co‐location patterns are still subjective. As a result, some co‐location patterns may be wrongly reported or omitted. Therefore, a nonparametric significance test without priori assumptions about the distributions of the spatial features is proposed in this article. Both point‐dependent and location‐dependent network‐constrained summary statistics are first utilized to model the distribution characteristics of the spatial features. Then, by using these summary statistics, a network‐constrained pattern reconstruction method is developed to construct the null model of the test, and the prevalence degree of co‐location patterns is modeled as the significance level. The significance test is evaluated using the facility points‐of‐interest data sets. Experiments and comparisons show that the significance test can effectively detect network‐constrained spatial co‐location patterns with less priori knowledge and outperforms two state‐of‐the‐art methods in excluding spurious patterns.     

携程旅行网旅游“新零售”空间格局及形成机制——以上海市线下门店为例

基于携程位于上海市的线下门店及相关POI 数据，运用 GIS 空间分析和地理探测器等方法，探讨携程旅游“新零售”空间格局、区位选址及形成机制。结果表明：①携程旅游“新零售”在发展初期，其区位选址仍具有向心性，但不如传统零售业对城市中心区位需求强烈，向中心城区边缘及城市近郊区域下沉趋势明显；②呈现集聚分布特征，形成“多中心-外扩”式分布模式；③区位选址依赖于以写字楼为主的线下生活服务场景，对商务办公区的偏好超越对商业中心的偏好。交通区位、传统旅行社分布、地租水平影响次之，CBD 趋近度、商圈等级影响较小；④携程线下门店空间格局及区位特征是企业业务板块规划、地理实体空间竞争、区域产业配套水平共同作用形成的结果。     

Matching Geospatial Concepts with Geographic Educational Needs

In this paper, we assume that learning to comprehend the geospatial environment would be significantly facilitated by developing a multi‐level task ontology that identifies various levels and complexities of geospatial concepts. We suggest that, apart from four spatial ‘primitives’– identity, location, magnitude, and space‐time – all geospatial concepts involve ‘inheritance’ characteristics. The more complex and abstract the concept, the larger the inheritance links that need to be appreciated to enhance concept understanding. For example, many basic geospatial concepts – such as direction and distance – are first‐order derivatives from the ‘location’ primitive, whereas concepts such as spatial association, map projection or interpolation are high‐order concepts that require several layers of geospatial concepts in their derivation. Having offered a five‐level ontology for concept organisation, we suggest sets of tasks that could establish an understanding of concepts, thus directly making the environment more legible in a spatial sense. We develop this framework in the context of the teaching of geography in grades from kindergarten to the final years of high school (grade 12 in the United States system). Our conceptualisation is grounded in the US school system – in which geography is usually absent in the curriculum.     

p‐Functional Clusters Location Problem for Detecting Spatial Clusters with Covering Approach

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.     

大都市中心区生产性服务业集群的空间溢出——以广州市天河区为例

空间溢出对产业集群形成发展具有重要意义。但空间溢出对生产性服务业集群的影响机制并不清楚。本文基于1 km×1 km网格和2013年广州市中心区--天河区的企业经济普查数据，采用空间数据分析技术，识别生产性服务业集群，分析集群特征，探测集群溢出类型。结果发现：①生产性服务业集群区位指向大城市中心区的核心地域，具有五种集群类型分异，空间呈不完全耦合。②生产性服务业集群空间规模受Jocobs型和MAR型两种溢出调控，在MAR型溢出作用下的集群空间规模相对较小。③信息服务业集群的溢出为负效应说明溢出作为一种潜在利益，在转化为集群优势的过程中存在过滤效应。     

Contagious Social Practice?

This paper explores the usefulness of spatial innovation diffusion theory for understanding the observed evolution of the social practice of attaining public support as a substitute for working. The empirical example chosen is sick leave. Several European countries have experienced a striking increase in costs for sick leave during the recent years, nowhere however as dramatic as in Sweden. Public costs for sick leave have doubled since 1997 while general health and life expectancy continually improve. No more than a small percentage of this change can be assigned to compositional changes in the population and labour market with respect to age, sex, education, location and sector of employment. Neither has economic benefit conditions changed substantially during this period. What essentially remain, as possible explanations, are changes in attitudes and practices among beneficiaries and physicians. One can observe substantial regional differences in the level of sick leave and apparent traces of spatial autocorrelation in the pattern. The spatial diffusion hypothesis was examined by help of a multivariate analysis based on socio‐economic panel data including sick leave benefits and location for each individual in the labour force 1996–2000. The analysis indicates that the neighbourhood effect is substantial on the local (290 municipalities) and the regional (21 counties) level. Largely, the regional diffusion effect remains when also controlling for more persistent average differences between counties. A semi‐public spatial attitude diffusion process, mainly operating at municipality and county levels, seems to be responsible for a substantial part of the observed increase in cost for sick leave in the period 1996–2000.     

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.     

Transnational Migration and the Economy of Funerals: Changing Practices in Ghana

Migrants are increasingly leading transnational lives, impacting the institutions that shape local economies both in their place of residence and in their home communities. One example of this is the institution of funerals in developing countries. Funerals are becoming multi‐sited events as migrants from developing countries play important roles in the organization, financing and practice of funeral ceremonies in their home countries. Funerals thus give rise to flows of money, goods and people across national borders, ultimately affecting different economies around the world. This article uses a multi‐sited research design to follow the flows associated with a funeral held in a village in the Ashanti Region of Ghana. Detailed data were collected simultaneously in four locations involved in the funeral, and a multiplier analysis was used to trace funeral spending in different locations and sectors. The analysis shows that funeral spending supports various economic sectors in Ghana and across the globe, reinforcing the nature of funerals as (partly) economic events, which should be included in economic analyses of remittances and migration. Funeral practices are modified in various ways to accommodate transnational elements. At the same time, funerals continue to act, even in a transnational context, as occasions for reaffirming ties and a sense of belonging; they form a way for home communities, both rural and urban, to keep migrants interested in them.     

Essai de typologie des centres de santé et de services sociaux au Québec

Given the importance of contextual factors—physical, social and institutional environments—for understanding health landscapes, this article examines the situation in the province of Québec and suggests a spatial typology at the scale of the health and social services centres (CSSS). These CSSS provide services for 95 areas which are the finest territorial delineation in terms of health policies since a reform instituted in 2003. While delivery of primary health and social services is defined at this local scale, overall health policy is decided at the provincial scale. The challenge for stakeholders is to supplement their local knowledge with that of the broader context. In this article, we use principal components analysis and hierarchical cluster analysis to identify eight profiles of CSSS. The final results of the cluster analysis demonstrate that two‐thirds of the health and social services centres correspond with two marginally differentiated profiles and the remaining third shows specificities that are highly spatially anchored.     

重大事件对城市空间结构的影响:研究进展与管理对策

在全球化竞争日益激烈的背景下,举办重大事件已成为许多城市进行重新定位或推动城市跨越式发展的一种途径。重大事件为综合运用结构主义、制度主义等多种方法研究城市空间结构的演化与重构提供了一个难得的契机,但国内外学术界关于重大事件对举办城市空间结构影响的专门研究还比较缺乏,特别是对于事件背景下城市空间要素怎样发生演变以及城市如何借助事件契机对空间结构进行优化的研究还很薄弱。本文通过梳理国内外有关重大事件对举办城市空间结构影响的文献,初步厘清了重大事件对举办城市空间结构的影响因素及作用方式,进而为事件举办城市提出了指导性的战略管理框架,并指出了现有研究存在的不足与未来发展方向。     

Does Spatial Clustering of Foreign Direct Investment Foster Global Production Networks? The Case of Qingdao,China

This article examines how spatial clustering of foreign direct investment (FDI) can foster a global production network (GPN). The cluster concept, in general, stressed inter-firm relations between local and regional firms while neglecting global–local linkages in the spatial context. In contrast, GPN focuses on the advantages of international production organization and governance structure but underestimates regional or localized channels of production arrangement. By integrating the conceptual framework of spatial clustering of FDI and GPN, this paper provides empirical evidence of various channels to knowledge and innovation transfer between domestic firms and foreign-invested enterprises. This paper demonstrates that the different types of linkages formation are key determinant factors for establishing cluster through contributing to local economic development.