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.     

Spatial and Scale‐Dependent Controls on North American Pan‐Arctic Minimum River Discharge

Spatial patterns of minimum monthly river discharge in the North American Pan‐Arctic and its potential controls are explored with geographically weighted regression (GWR). Minimum discharge is indicative of soil water conditions; therefore, understanding spatial variability of its controls may provide insights into patterns of hydrologic change. Here, GWR models are applied to determine a suitable combination of independent variables selected from a set of eight variables. A model specification with annual mean river discharge, temperature at time of minimum discharge, and biome describes well the spatial patterns in minimum discharge. However, minimum discharge in larger watersheds is influenced more by temperature and biome distributions than it is in small basins, suggesting that scale is critical for understanding minimum river discharge. This study is the first to apply GWR to explore spatial variation in Pan‐Arctic hydrology. Factores de control espaciales y dependientes de escala en las descargas fluviales mínimas de ríos Pan‐Articos en Norteamérica. El artículo explora los patrones espaciales de caudales fluviales mínimos mensuales la región pan‐ártica de Norteamérica y sus posibles factores de control haciendo uso de una regresión ponderada geográficamente (geographically weigted regression‐GWR). Los caudales mínimos son indicadores de las condiciones del agua en el suelo, y por lo tanto el entendimiento de la variabilidad espacial de los factores que los controlan puede ayudar a comprender los patrones de cambio hidrológico. En el presente estudio, varios modelos de tipo GWR son aplicados para determinar una combinación adecuada de variables independientes seleccionadas a partir de un conjunto de ocho variables. El modelo que utiliza la media anual media de descarga fluvial, la temperatura en el momento de caudal mínimo, y el bioma, proporciona una buena descripción de los patrones espaciales en la descarga mínima. Sin embargo, en las cuencas hidrográficas grandes, la descarga mínima está más influenciada por la temperatura y la distribución de los biomas que en el caso de cuencas más pequeñas, lo que sugiere que la escala es fundamental para entender la descarga mínima fluvial. Este estudio es el primero en aplicar GWR para comprender la variación espacial en la hidrología de la región pan‐ártica. 基于GWR（地理加权回归模型）对北美泛北极地区月份最小河流流量的空间模式和潜在控制进行研究。最小流量暗示水土条件;因此,理解空间分异及控制可深刻理解水文变化的模式。GWR可从8个变量中提取一组独立变量的适当组合。通过年均河流流量、最小流量时的温度和生物群落,来描述最小下泄流量的空间格局。在大范围流域中,最小流量受到温度和生物群落分布的影响大于在小规模的流域,揭示出在河流最小流量分析中尺度是非常重要的。本文首次将GWR应用于泛北极水文空间异质性分析。     

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.     

A Geostatistical Framework for Area‐to‐Point Spatial Interpolation

The spatial prediction of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support; the term support refers to the domain informed by each datum or unknown value. It is demonstrated that the proposed geostatistical framework can explicitly and consistently account for the support differences between the available areal data and the sought‐after point predictions. In particular, it is proved that appropriate modeling of all area‐to‐area and area‐to‐point covariances required by the geostatistical frame‐work yields coherent (mass‐preserving or pycnophylactic) predictions. In other words, the areal average (or areal total) of point predictions within any arbitrary area informed by an areal‐average (or areal‐total) datum is equal to that particular datum. In addition, the proposed geostatistical framework offers the unique advantage of providing a measure of the reliability (standard error) of each point prediction. It is also demonstrated that several existing approaches for area‐to‐point interpolation can be viewed within this geostatistical framework. More precisely, it is shown that (i) the choropleth map case corresponds to the geostatistical solution under the assumption of spatial independence at the point support level; (ii) several forms of kernel smoothing can be regarded as alternative (albeit sometimes incoherent) implementations of the geostatistical approach; and (iii) Tobler's smooth pycnophylactic interpolation, on a quasi‐infinite domain without non‐negativity constraints, corresponds to the geostatistical solution when the semivariogram model adopted at the point support level is identified to the free‐space Green's functions (linear in 1‐D or logarithmic in 2‐D) of Poisson's partial differential equation. In lieu of a formal case study, several 1‐D examples are given to illustrate pertinent concepts.     

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.     

A Comparison of Three Exploratory Methods for Cluster Detection in Spatial Point Patterns

This paper compares the performances of three exploratory methods for cluster detection in spatial point patterns where the at-risk population is known. After reviewing two existing methods, Openshaw et al. (1987) and Besag and Newell (1991), an alternative method is introduced. These three methods are then compared empirically using two point patterns drawn from a disaggregate housing database consisting of 28,832 observations. Each observation in the data set contains attributes of single-family detached dwellings in the City of Amherst, New York. This paper provides some new insights into the performance of the three methods, as previous applications have used spatially aggregated (and hence rather inaccurate) data. The paper also demonstrates the utility of GIS for this type of spatial analysis.     

Spatial and Temporal Patterns of Recreational Angling Effort in a Warm‐Temperate Australian Estuary

Among the critical issues for the management of estuarine fish resources is the need to adequately identify the extent of use by recreational fishers, and the allocation of the resource between recreational and commercial users. Recreational anglers in estuaries often target a similar range of estuarine fish species to commercial fishers. Prior to the declaration of ‘recreational fishing havens’ (estuarine waters closed to commercial netting but remaining open to recreational angling) in many New South Wales (NSW) estuaries in May 2002, the estuary of the Manning River on the central NSW coast supported the tenth most productive commercial estuarine net fishery in the State. To quantify the recreational angling effort in the Manning River estuary, progressive counts were made of shore‐based and boat‐based anglers from a small power boat from June 2001 to May 2002. Sample days (eight per month) were chosen in a stratified random fashion, taking into account the proportions of weekends, public holidays and school holidays in each month. The total recreational angling effort exerted on the estuary for the 12‐month period was 144 892 ± 3248 angler‐hours, indicating a low to medium effort compared to other NSW estuaries of similar size. Over half of the effort was exerted in the section nearest the northern entrance at Harrington. There was relatively little variation in shore‐based angling effort throughout the year, whereas boat‐based fishing increased markedly in summer. Months that coincided with NSW school holidays were most popular, especially January. More recreational fishing effort was exerted in the afternoons than in the mornings during winter, but this trend was reversed in summer. Although daily effort was much higher on weekends than weekdays, the total cumulative effort was generally similar, due to the greater number of weekdays in the year. Pilot studies of catch per unit effort indicate that the recreational catch in the Manning River estuary is probably small compared to the pre‐closure commercial catch. Therefore management strategies aimed at limiting the recreational catch are likely to have minimal impact on fish stocks in the estuary when compared with regulation of the commercial sector.     

The Spatial Patterns of Networks,Hierarchies and Subsidiaries

This article reviews evidence and arguments linking the networking behaviour of firms with geographic distance, before examining the spatial relationships of electronics firms in the three major electronics centres in Spain. The focus is on the spatial pattern and extent of different types of inter-firm relations. Based on the analysis of 184 surveyed establishments, displaying different ownership and organization characteristics, the results show that while regional linkages are important, significant extra-regional linkages are also maintained by firms in regional clusters. The spatial extent of linkages depends on the mode of relations; arm's length, network and hierarchy relations show different spatial patterns, as do different types of cooperation. The importance of extra-regional linkages also varies with firm- and plant-specific characteristics. Extra-regional linkages are more common among larger and more R&D-intensive firms, firms with greater presence in the rest of the country and firms with more experience of cooperation and more stable relationships.     

Spatio‐Temporal Modelling As A Way to Reconstruct Patterns of Past Human Activities

This paper examines the possibilities of creating quantified models of past human activities in both time and space. The study area lies in the south‐eastern Czech Republic and western Slovakia. The spatio‐temporal model of behavioural categories was calculated with the help of Monte Carlo simulations and statistical testing. One of the main advantages of our approach is that it admits the probabilistic nature of input data, quantifies them and provides probabilistic results comparable with other proxies. It also presents a less biased way of how archaeological data from regions and periods with low numbers of ¹⁴C datings can be incorporated into models of past population dynamics.     

Identifying the Impacts of Land-Use Spatial Patterns on Street-Network Accessibility Using Geospatial Methods

While the land use-street network nexus is well acknowledged, evidence for the one-way impacts of land-use patterns on street accessibility is still inadequate. The measurements of land-use patterns and street accessibility lack systematic knowledge. Their empirical correlations also lack geographical variability, constraining site-specific land-use practices. Therefore, this study overcame the aforementioned limitations by examining the two-level spatial models to formulate accessibility-oriented land plans, using a well-developed Chinese city as an example. Firstly, two landscape metrics—Euclidean Nearest-Neighbor Distance (ENN) and Similarity Index (SIMI)—were used to quantify the intra- and inter-land-use configurations, respectively. Both city-level and local accessibility were measured using spatial design network analysis. Performing both ordinary least squares (OLS) and geographically weighted regression (GWR) models, results identified the statistically significant effects of inter-land-use patterns on two-level street accessibility. An exception was that land-use configurations within residential and industrial regions were irrelevant to street accessibility. We also found GWR was a better-fitting model than OLS when estimating locally-varied accessibility, suggesting hierarchical multiscale land-use planning. Overall, locally heterogeneous evidence in this study can substantialize land use-street network interactions and support the decision-making and implementation of place-specific accessibility-oriented land use.     

ART‐P‐MAP Neural Networks Modeling of Land‐Use Change: Accounting for Spatial Heterogeneity and Uncertainty

Spatial land‐use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART‐Probability‐Map (ART‐P‐MAP), tailored to address these issues in the context of spatial modeling of land‐use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell‐level uncertainty measurement. Compared with two baselines, ART‐P‐MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land‐use change problem. Finally, we conclude that, as a general probabilistic regression model, ART‐P‐MAP is applicable to a broad range of land‐use change modeling approaches, which deserves future research.     

Investigating Private Motorised Travel and Vehicle Fleet Efficiency: Using New Data and Methods to Reveal Socio‐Spatial Patterns in Brisbane,Australia

Australian cities have seen continued long‐term growth in private motor vehicle travel that has imposed increasing vehicle energy consumption and greenhouse gas emissions. This paper investigates the spatial patterns of vehicle energy consumption on urban areas through an analysis of vehicle travel and of vehicle fleet efficiency in Brisbane, Australia. This is achieved by combining motor vehicle registration records and Australian government's ‘Green Vehicle Guide’ of vehicle fuel efficiency database. The results of a spatial analysis of the private vehicle trip distances derived from journey to work data and fuel energy consumption associated with the private‐owned vehicles decomposed to local areas show that private vehicle energy use tends to increase with increasing distance from the city centre (e.g. central business district). This analysis demonstrates that differences in vehicle trip distances and lower proportions of high‐efficiency vehicles in the outer suburbs aggravate vehicle energy consumption in those locations. The paper further compares vehicle energy use results for Brisbane against spatial patterns of suburban socio‐economic disadvantage. The paper demonstrates that access to vehicle fleet technology may compound other forms of socio‐economic disadvantage and vulnerability.     

Transnational Flows of E-Waste and Spatial Patterns of Recycling in China

Two geographers residing in Beijing discuss the inflows, processing, and consumption of electronic waste—a topic largely neglected in the current literature on globalization. Based on extensive interviews with electronics producers and recyclers in China, the paper explores the global flows of e-waste and concentration of related recycling in coastal China. The authors suggest that recycling activities (authorized as well as illegal) grew in tandem with the dramatic increase in electronics production during the last decade. They note that the country's recycling sector has played a significant role in rural industrialization and local economic development, albeit in conflict with the objectives of environmental protection. Journal of Economic Literature, Classification Numbers: F20, L63, O17, O19. 6 figures, 23 references.     

Nonlinear Multivariate Spatial Modeling Using NLPCA and Pair‐Copulas

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.     

Spatial equilibrium in deviations: An application to skill‐premium and skill‐mix heterogeneity

In this paper, we propose a novel—and general‐purpose—modeling approach. We give a linear representation of the spatial general equilibrium, expressed in terms of local percentage deviations from the benchmark case of symmetry, where all the areas in the economy are taken to be initially identical. To illustrate the flexibility of our approach, we revisit the literature on the spatial heterogeneity of local skill premia and local skill mix. We show that our approach is able to encompass a variety of alternative explanations in a simple “unifying framework.” Finally, we exploit a graphical version of the model to show how to implement empirical tests.     

Spatial Representations of Habitat in Competition‐Colonization Models

Ecological models with species differentiated by competition and colonization traits have shown that some extant species in remnants of habitat go extinct after a lag. These models have, however, analyzed landscape patterns in two phases: habitat and nonhabitat. Here, the consequences of representing landscape patterns as a continuous surface of habitat quality versus as two categories are examined. With a continuous representation, the amount of habitat is constant at 100 percent, but its quality can vary from 0 to 1; with a binary representation the amount of habitat varies. Continuous landscapes with mean habitat qualities of .8, .5, and .2 are compared to binary landscapes with the proportion of habitat at .8, .5, and .2. The model projections of the abundances of species differ substantially between the two‐phase and continuous representations. The effects of decreased habitat quality with no decrease in abundance exceed the effects of decreased habitat abundance. Differences between the projections for the two representations increase as the proportion of habitat and habitat quality decrease. Increases in the variance of habitat quality within the continuous representations decrease extinctions. The basic insight of earlier models, that superior competitors are at a long‐term disadvantage in remnant habitat, is magnified with a continuous representation. As in the binary models, the disadvantage is lessened if the spatial variation in habitat quality is smoother, but pattern matters less in continuous landscapes. The continuous representation shows that lagged extinction is relevant to cases of habitat deterioration.