Spatial Causality: A Systematic Review on Spatial Causal Inference

The growing interest in causal inference in recent years has led to new causal inference methodologies and their applications across disciplines and research domains. Yet, studies on spatial causal inference are still rare. Causal inference on spatial processes is faced with additional challenges, such as spatial dependency, spatial heterogeneity, and spatial effects. These challenges can lead to spurious results and subsequently, incorrect interpretations of the outcomes of causal analyses. Recognizing the growing importance of causal inference in the spatial domain, we conduct a systematic literature review on spatial causal inference based on a formal concept mapping. To identify how to assess and control for the adverse effects of spatial influences, we assess publications relevant to spatial causal inference based on criteria relating to application discipline, methods used, and techniques applied for managing issues related to spatial processes. We thus present a snapshot of state of the art in spatial causal inference and identify methodological gaps, weaknesses and challenges of current spatial inference studies, along with opportunities for future research.     

Spurios Spatial Regression: Some Monte Carlo Results with a Spatial Unit Root and Spatial Cointegration

In this paper I introduce the concepts of spatial unit roots and spatial cointegration, and via Monte-Carlo simulation I illustrate their implications for spatial regression. It is shown that spatial unit roots lead to spurious (spatial) regression, as in the well-known case involving time-series. Spatial cointegration is similar to its time-series counterpart, although I demonstrate that OLS estimation of spatial error-correction models is not consistent.     

The Authenticity of Spatial Planning Knowledge

This article intends to start a debate on the authentic disciplinary grounds of spatial planning studies as a scientific discipline of spatial intervention closely interrelated with practices of spatial development. In a volatile epoch of regrouping academic specializations, scientists feel a strong incentive to reflect on the authentic grounds of their discipline. This certainly goes for spatial planning studies that heavily rely on intensive interaction with a wide range of disciplines. What justifies its own position? Spatial planning studies are intensely interrelated with practices of planning, which demarcates its scientific position in a specific way. The author does not claim an exclusive position for spatial planning studies (the diversity of external relations and alliances enriches the field of study) but defines the authentic identity of the spatial planning discipline in the conceptual coherence of five dimensions: practical context, spatial norms, knowledge and action, to be validated in new contexts of practice. The author states that different planning paradigms interconnect the five dimensions in their own way and in doing so innovate the authentic body of planning knowledge. To invest in this authenticity is crucial in this era of organizational proliferation.     

Spatial Governance across Borders Revisited: Organizational Forms and Spatial Planning in Metropolitan Cross-border Regions

In Europe, cross-border cooperation in spatial planning has intensified in recent years. Organizations with varying institutional characteristics have emerged in urban areas that cross a national border. They deal with problems stemming from the cross-border situation. The institutional and spatial aspects of organizations on the sub-national level in nine European cross-border metropolitan regions are compared in this article. The analytical concept of metropolitan governance applied here takes into account the simultaneous existence of different cross-border organizations as well as their spatial and functional relationships. Subsequently, this article assesses the impact of governance on spatial planning in two cases, Basel and Lille, where institutional changes occurred on the sub-national level between 2000 and 2010. The way organizational and spatial attributes of new forms of cross-border metropolitan governance influence the coordination of spatial policies is discussed here. The comparison of organizational characteristics and capacity to coordinate reveals important differences. Furthermore, comparing spatial scales shows how new cross-border perimeters are drawn along existing national territories. Organizations' competences and interests are decisive for the coordination and implementation of spatial policies.     

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.     

Substitutability,Spatial Structure,and Spatial Interaction

This paper attempts to distill the relationship among spatial structure, consumer preferences, and spatial interaction. It introduces the notion of substitutability. Destinations are substitutable in both economic-behavioral and physical-locational senses. Inadequate treatment of this can lead to variations in the estimated parameters of conventional spatial interaction models.     

Measuring Spatial Correspondence among Network Paths

Measuring the spatial correspondence among geographic features is an important component of many analyses such as those seeking to identify similar or dissimilar features, explore the extent to which change has taken place, and explain the processes influencing spatial change. In networked systems, the paths providing connectivity between pairs of nodes are often the geographic features among which spatial correspondence is to be assessed. That is, given a set of paths between a pair of nodes, the extent to which they share portions of the network and spatially deviate from one another can provide insight on the factors underlying the use of a system. While methods for measuring the spatial correspondence among specific types of network paths have been devised, this article proposes a methodology for measuring the spatial correspondence among the topology of network paths of any type, including those involving redundant use of network space. By basing comparisons upon the topologic relationships among the paths, the proposed approach better accounts for average spatial deviation as well as the asymmetric spatial relationship between pairs of paths, enabling greater stability and consistency in the analysis of their spatial characteristics. The developed methodology is applied to evaluate variability in spatial correspondence among a set of network paths to demonstrate its utility.     

Spatial segregation and class subjectivity in Turkey

Drawing on ethnographic research in an upper-class district in Turkey, this article examines social and spatial experiences of young low-wage service workers who travel daily back and forth between their homes in low-income neighbourhoods and their jobs in gated communities, upscale shopping malls and corporate offices. The paper argues that the significance of upper class districts or gated communities for urban inequality lies not in the sites themselves, nor the lifestyles of their elite inhabitants as commonly treated in the literature, but rather in the ways in which they relate to the outside and outsiders. Within this framework, the paper analyses the district’s effect on urban spatial segregation and urbanites’ sense of place in society. While resentment and reaction to inequalities and upper class customers are prevalent in young workers’ narratives, workers’ class subjectivities are also marked by a sense of mobility and liminality between the upper middle classes in their work district and their families, friends and neighbours back home. This sense of socio-spatial ‘in-betweenness’ is reinforced by being young, hence a sense of temporal liminality between youth and adulthood. The study contributes to the understanding of urban inequality at the intersection of spatial, emotional and temporal experiences of urbanites.     

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.     

Spatial Analysis of Regional Inequalities in Turkey

In this paper, we examine regional inequalities in Turkey not only at the interprovincial level but for three different regional definitions as well. It has raised questions about inequalities not only between regions (interregional) but inequalities within each region. Hence, one contribution of this paper is to test the effects of aggregation and scale on the identification of regional inequalities using currently accepted spatial analytic methods. The results indicate that overall inequalities are decreasing; however spatial dependence is becoming more dominant. The Theil Index indicates that interregional inequalities are increasing while intraregional inequalities are declining for all spatial partitions from 1980 to 1997. Most developed provinces are enhancing overall inequalities, although there is some evidence of a spread effect on their neighbours.     

Spatial Spillovers and Regional Growth: A Cognitive Approach

Spatial spillover effects are widely studied in the literature, as an important source of externality and therefore of discrepancy between private and social optimum, creating the emergence for ad-hoc policy interventions. Within the literature, their spatially-bounded nature is highly emphasised, but the mechanisms for their spatial diffusion are generally considered as a black box, with no reference to territorialized channels through which these externalities spread around. The study aims to present a literature review on the spatial spillover concept, emphasising two main stages of reflection, the so-called spatial approach and the functional approach. This work identifies in a third approach, the cognitive approach, a new perspective to the interpretation of spatial spillovers and their conditions, and on this approach it elaborates theoretically and empirically. The main assumption is that the capabilities of economic agents to absorb and utilize spillovers—implicitly assumed by the spatial spillover theory to be invariant with respect to space—in reality highly depend on strongly territorialized and un-imitable assets, such as local trust, sense of belonging and creativity. This assumption is tested empirically on all NUTS 2 of the 27 European member countries, thanks to a regional forecasting growth model developed by the author, called MASST.     

Spatial Entropy

A major problem in information theory concerns the derivation of a continuous measure of entropy from the discrete measure. Many analysts have shown that Shannon's treatment of this problem is incomplete, but few have gone on to rework his analysis. In this paper, it is suggested that a new measure of discrete entropy which incorporates interval size explicitly is required; such a measure is fundamental to geography and this statistic has been called spatial entropy. The use of the measure is first illustrated by application to one-and two-dimensional aggregation problems, and then the implications of this statistic for Wilson's entropy-maximizing method are traced. Theil's aggregation statistic is reinterpreted in spatial terms, and finally, some heuristics are suggested for the design of real and idealized spatial systems in which entropy is at a maximum.     

Levels of Spatial Knowledge and Urban Travel Modeling

Models of spatial behavior implicitly assume a direct connection between the individual's utility function and his actual behavior. In reality, this link is mediated by the extent and quality of his spatial knowledge. Without sufficient knowledge, the chosen behavior will be selected from a small number of known alternatives. Using a route choice study in Beer Sheva, this paper investigates the extent and the level of spatial knowledge exhibited by the public in episodic, nonroutine travel. It demonstrates that their knowledge stabilizes at the intermediate “route level,” whereas only professional drivers attain the highest “survey level.” The cognitive opportunity sets of drivers at the route level are idiosyncratic. Accordingly, route selection by the general public was found largely unpredictable. Implications for urban travel modeling are discussed.     

Exploring Relationships Between the Global and Regional Measures of Spatial Autocorrelation

Abstract The objective of this research is to investigate dimensions of geographic variation in spatial dependency contained within large multilevel data sets. We calculate 1990 population density by census block group, county, and state for the 48 coterminous states and the District of Columbia of the United States, calculations of interest to a wide variety of spatial scientists. We explore relations between these levels and their variation across the nation. The empirical findings generated by this work furnish implications concerning the Modifiable Areal Unit Problem (MAUP), spatial autocorrelation statistics, scale effects, and resolution.     

Interpreting Spatial Economic Structure and Spatial Multipliers: Three Perspectives

The measurement and interpretation of spatial economic structure have presented many problems for analysts. The notion of a spatial multiplier has received even less formal attention; part of the motivation for this paper is the examination of the degree to which sectoral decomposition analysis might prove useful in illuminating the paths of interaction across space that are associated with changes in exogenous activity levels. In this paper, we present three perspectives that examine spatial structure in the context of an aggregated interregional social accounting matrix for Bangladesh. These perspectives examine structure at the micro-, meso-, and macrolevels and are interpreted with the assistance of several alternative methods, including structural path analysis, the field of influence, the superposition principle, multiregional feedback loop analysis, and the Matrioshka Principle; the results are interpreted analytically and cartographically. Some attention is also devoted to the formulation of a general approach to spatial multipliers. It is shown that the structure of flows and the structure of influence reveal patterns that, taken together, provide important insights into an understanding of structure and may prove to form the basis for the eventual formal presentation of the spatial multiplier.     

The SpatialARMED Framework: Handling Complex Spatial Components in Spatial Association Rule Mining

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.     

Serbian Spatial Planning among Styles of Spatial Planning in Europe

The classification of spatial planning styles in Europe has been successively reviewed a few times. Most recently, it has been done in the framework of the European Spatial Planning Observatories Network (ESPON) 2.3.2 project dealing with the territorial governance. Based on national overviews for 29 European countries, this project uses the classification of spatial planning systems in four styles: comprehensive-integrated, regional-economic, land-use and urbanism. This study did not take into account the spatial planning system in Serbia since it is neither in the European Union nor a member of the ESPON similar to Norway and Switzerland. This article uses the form of national overviews elaborated for the ESPON project and puts Serbia in a comparative spatial planning perspective, classifying it between the comprehensive-integrated and land-use planning styles.     

Spatial Autoregressive Models for Geographically Hierarchical Data Structures

This article discusses how standard spatial autoregressive models and their estimation can be extended to accommodate geographically hierarchical data structures. Whereas standard spatial econometric models normally operate at a single geographical scale, many geographical data sets are hierarchical in nature—for example, information about houses nested into data about the census tracts in which those houses are found. Here we outline four model specifications by combining different formulations of the spatial weight matrix W and of ways of modeling regional effects. These are (1) groupwise W and fixed regional effects; (2) groupwise W and random regional effects; (3) proximity‐based W and fixed regional effects; and (4) proximity‐based W and random regional effects. We discuss each of these model specifications and their associated estimation methods, giving particular attention to the fourth. We describe this as a hierarchical spatial autoregressive model. We view it as having the most potential to extend spatial econometrics to accommodate geographically hierarchical data structures and as offering the greatest coming together of spatial econometric and multilevel modeling approaches. Subsequently, we provide Bayesian Markov Chain Monte Carlo algorithms for implementing the model. We demonstrate its application using a two‐level land price data set where land parcels nest into districts in Beijing, China, finding significant spatial dependence at both the land parcel level and the district level.     

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.     

Exploring Spatial Patterns Using an Expanded Spatial Autocorrelation Framework

Spatial autocorrelation (SA) is regarded as an important dimension of spatial pattern. SA measures usually consist of two components: measuring the similarity of attribute values and defining the spatial relationships among observations. The latter component is often represented by a spatial weights matrix that predefines spatial relationship between observations in most measures. Therefore, SA measures, in essence, are measures of attribute similarity, conditioned by spatial relationship. Another dimension of spatial pattern can be explored by controlling observations to be compared based upon the degree of attribute similarity. The resulting measures are spatial proximity measures of observations, meeting predefined attribute similarity criteria. Proposed measures reflect degrees of clustering or dispersion for observations meeting certain levels of attribute similarity. An existing spatial autocorrelation framework is expanded to a general framework to evaluate spatial patterns and can accommodate the proposed approach measuring proximity. Analogous to the concept of variogram, clustergram is proposed to show the levels of spatial clustering over a range of attribute similarity, or attribute lags. Specific measures based on the proposed approach are formulated and applied to a hypothetical landscape and an empirical example, showing that these new measures capture spatial pattern information not reflected by traditional spatial autocorrelation measures.