Spatial dependence and the representation of space in empirical models

A well-formed spatial model should most likely not produce spatial autocorrelation at all. From this perspective spatial autocorrelation is not (pure) statistical nuisance but a sign of that a model lacks a representation of an important economic phenomenon. In a Knowledge Production Function (KPF) context, this paper shows that a representation of space reflecting the potential of physical interaction between localities by means of accessibility variables on the “right-hand-side”—a simple alternative to spatial lag and spatial error which can be estimated by OLS—captures substantive spatial dependence. Results are verified with Monte Carlo simulations based on Anselin’s (Int Reg Sci Rev 26(2):153–166, 2003) taxonomy of modelled and unmodelled effects. The analysis demonstrates that an accessibility representation of explanatory variables depict the network nature of spatial interaction, such that spatial dependence is actually modelled.     

The effects of labor market spatial structure and the built environment on commuting behavior: Considering spatial effects and self-selection

In response to the problems caused by urban sprawl, smart growth strategies have gained increasing popularity among researchers and policy makers. Although numerous studies have identified the importance of land use policies on travel behavior, the effects are still inconclusive mostly due to two issues: residential self-selection and spatial spillover effect. Using instrumental variables with a generalized spatial two-stage least square estimator (GS2SLS), I investigate the effect of land use on travel behavior after controlling for the self-selection problems and spatial autocorrelation together. Particularly, I attempt to explain the effects of not only land use variables, but also neighborhood labor market spatial structures on commuting patterns. The findings clearly reveal that labor market spatial structure and land use play a significant role in determining commuting behavior at an aggregate level. Specifically, higher jobs-housing balance, more retail and service jobs in a neighborhood, higher density, and walker-friendly environment reduce commuting time and car use and increase non-motorized travel.     

Configuration of sample points for the reduction of multicollinearity in regression models with distance variables

Regression models often suffer from multicollinearity that greatly reduces the reliability of estimated coefficients and hinders an appropriate understanding of the role of independent variables. It occurs in regional science especially when independent variables include the distances from urban facilities. This paper proposes a new method for deriving the configuration of sample points that reduces multicollinearity in regression models with distance variables. Multicollinearity is evaluated by the maximum absolute correlation coefficient between distance variables. A spatial optimization technique is utilized to calculate the optimal configuration of sample points. The method permits us not only to locate sample points appropriately but also to evaluate the location of facilities from which the distance is measured in terms of the correlation between distance variables in a systematic way. Numerical experiments and empirical applications are performed to test the validity of the method. The results support the technical soundness of the proposed method and provided some useful implications for the design of sample location.     

The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.     

Spatial heterogeneities,institutions, and income: Evidence for Brazil

Political institutions are an essential component to explain income variation. Brazilian municipalities are characterized by a great contrast in the colonization process and its impacts on the current income level and distribution, and other development aspects. This study analyzes the spatial heterogeneity of the relationship between institutional quality and the municipalities' economic development. We use a spatial moving window method applied to weighted regressions—Geographically Weighted Regression (GWR). We find evidence that good institutions increase income and other variables related to economic development. In addition, the institutional quality influence on economic development measures is distinct across the Brazilian regions.     

Quality of government and regional resilience in the European Union. Evidence from the Great Recession

This paper examines the relationship between quality of government and regional resilience in the European Union during the Great Recession. The results show that the quality of government is an important factor when shaping the regional reaction to the crisis. Our estimates reveal that higher quality of government is associated with greater regional resilience over the Great Recession. This is partly due to the role played in this context by spatial spillovers induced by the quality of government in neighbouring regions. The observed link between governance and regional resilience is robust to the inclusion in the analysis of different explanatory variables that may affect both government quality and regional resilience. Likewise, our findings do not depend on the specific dimension of governance considered, the estimation method or the econometric specification employed to capture the nature of spatial spillovers.     

The effects of built environment on community participation in urban neighbourhoods: an empirical exploration

Attention to the physical characteristics of the urban environment is the fundamental aspect of urban studies. Despite the many theoretical contributions that explore the interaction between space and social phenomena from a theoretical point of view, we still find today the absence of a general empirical reflection on this issue. In this article the results of the doctoral thesis of the author will be presented, where the relationship between the socio-physical characteristics of the neighbourhood and community participation has been studied. The reason why we focused on the community participation lies on the need to explore the role played by urban environment in affecting the propensity by dwellers to participate to local activities. Since most of literature on community participation draw the attention only to the socio-demographics factors, we want to stress the impact of socio-physical variables in affecting the phenomenon here presented. The research has been carried out using the contribute of environmental psychology, from which we took the methodology in order to address the study of the interaction between humans and environments. Multivariate regression models show a high explanatory capacity of spatial variables on community participation and the effect of sociodemographic variables is in fact marginal. The results of the analyses show that the socio-physical characteristics of the neighbourhoods studied are not a passive background of the social phenomenon, but are variables that intervenes directly in. The research thus reveals the active role played by spatial factors in neighbourhoods when local participation processes occur In the neighbourhoods, improving the scientific debate on the community participation and urban studies.     

Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation

Traditional regression techniques such as ordinary least squares (OLS) can hide important local variations in the model parameters, and are not able to deal with spatial autocorrelations existing in the variables. A recently developed technique, geographically weighted regression (GWR), is used to examine the relationships between land use and water quality in eastern Massachusetts, USA. GWR models make great improvements of model performance over OLS models, which is proved by F-test and comparisons of model R² and corrected Akaike Information Criterion (AIC_c) from both GWR and OLS. GWR models also improve the reliabilities of the relationships by reducing spatial autocorrelations. The application of GWR models finds that the relationships between land use and water quality are not constant over space but show great spatial non-stationarity. GWR models are able to reveal the information previously ignored by OLS models on the local causes of water pollution, and so improve the model ability to explain local situation of water quality. The results of this study suggest that GWR technique has the potential to serve as a useful tool for environmental research and management at watershed, regional, national and even global scales.     

Using Micro-Level Data to Evaluate Infrastructure of the Walking Environment Around Bus Rapid Transit Stations:A Case Study of Xiamen,China

China has experienced rapid car-oriented suburbanization during the past decades and is facing emerging challenges such as traffic congestion,air pollution,and even health threats.Learning from North American cities,Chinese cities have started adopting and implementing a Transit-Oriented Development(TOD) strategy,which encourages the transit ridership and walking trips by creating a pedestrian-friendly urban environment.Although much research has examined the walking environment in various neighborhoods or communities,few existing studies have examined the walking environment around transit stations,especially using micro-level data,such as walking infrastructure variables.This research chooses several infrastructure variables that are critical for pedestrians to investigate the walking environment around stations of the Bus Rapid Transit(BRT) No.1 Line in Xiamen City,China.This study defines a half mile buffer centered on each station as the spatial unit of analysis.The Geographic Information System(GIS) and field audits are employed to measure the characteristics of the infrastructure for pedestrians in each spatial unit of analysis.The results of this analysis can help assess the existing conditions of the infrastructure for pedestrians in each station area,which might need to be added or improved.The findings will not only provide empirical support for improving TODs in Xiamen,but also offer a lesson on how areas around transit stations could be retrofitted to improve the pedestrian environment.     

An approach for simulating the street spatial patterns

The form of the urban fabric plays an important role in urban morphology which not only expresses the city’s overall morphological features, but also describes the relationships between buildings and plots, which are called patterns. The majority of morphological studies have focused on the street network pattern, the plot pattern and the building pattern or typology. In the other hand, the form of the urban fabric also reflects the city’s space character, such as street spatial outline, visual variety, and spatial typologies, which are directly related to the quality of urban space. Although these characters influence the urban design decision-taking, the current research has not reached an effective level. With a special focus on potentials for urban design, this paper proposes an account of how building position affects the street spatial pattern. Based on the viewshed analysis in GIS, the variance between the form of the urban fabric and the street viewshed pattern could be measured. The study samples were 600 meters square samples of urban fabric image selected from different cities of Europe, America, and China. This paper proposes a new kind of pattern: street spatial related line (SSRL) pattern. Through viewshed simulating it shows that there is a strong relationship between SSRL pattern and street spatial configuration, and that visual statistical diagrams could indicate the street spatial characters.     

Human sources of landscape stewardship: the re-emergence of private farming in the Czech Republic

Historically, an important role informing the Czech cultural landscape was played by the strong middle class of farmers. The period of collectivization which occurred progressively between 1950 and 1989 meant liquidation of the rural community. Whereas in 1950 there were 1,404,225 private farmers, this number had decreased to only 2,000 in 1988. This paper focuses on the problems of stewardship and responsibility towards landscape associated with this social group, which recently has started a new period of growth. The research uses a variety of qualitative and quantitative methods. Dialogues with interviewees have enabled us to interpret stewardship and responsibility into a coherent story about the relationship between people and landscape. Similarities and differences have been related to variables such as farmer's age and length of time in farming. The results show that patterns of stewardship and responsibility are not related to economic variables, but rather to common features of 'timeless' rural culture.     

Spatiotemporal relationship between particle air pollution and respiratory emergency hospital admissions in Brisbane, Australia

The nature of spatial variation in the relationship between air pollution and health outcomes within a city remains an open and important question. This study investigated the spatial variability of particle matter air pollution and its association with respiratory emergency hospital admissions across six geographic areas in Brisbane, Australia. Data on particles of 10 microm or less in aerodynamic diameter per cubic metre (PM10), meteorological conditions, and daily respiratory emergency hospital admissions were obtained for the period of 1 January 1998 to 31 December 2001. A Poisson generalised linear model was used to estimate the specific effects of PM10 on respiratory emergency hospital admissions for each geographic area. A pooled effect of PM10 was then estimated using a meta-analysis approach for the whole city. The results of this study indicate that the magnitude of the association between particulate matter and respiratory emergency hospital admissions varied across different geographic areas in Brisbane. This relationship appeared to be stronger in areas with heavy traffic. We found an overall increase of 4.0% (95% confidence interval [CI]: 1.1-6.9%) in respiratory emergency hospital admissions associated with an increase of 10 microg /m3 in PM10 in the single pollutant model. The association was weaker but still statistically significant (an increase of 2.6%; 95% CI: 1.0-5.5%) after adjusting for O3, but did not appear to be affected by NO2. The effect estimates of PM10 were generally consistent for three spatial methods used in this study, but appeared to be underestimated if the spatial nature of the data was ignored. Therefore, the spatial variation in the relationship between PM10 and health outcomes needs to be considered when the health impact of air pollution is assessed, particularly for big cities.     

Impacts of national development and decentralization policies on regional income disparity in Korea

This paper analyzed the effects of national development and decentralization policies on the regional income disparity in Korea. It also attempted to test whether there was a structural change in the causal relationship between income inequality and its determinant factors. This study found that the degree of variation in regional incomes was positively correlated with the spatial distribution of decentralization instruments such as educational services, employment, infrastructure facilities, and information network variables. The effects of some decentralization policies on regional income inequality were fully realized within a period of three years. Finally, the impacts of the spatial decentralization of public goods on regional inequality fluctuated until the early 1980s but were stabilized as industrial restructuring and spatial reorganization progressed during the 1990s. Received: December 2001/Accepted: June 2002     

Homeownership,Asset-based Welfare and the Neighbourhood Segregation of Wealth

The asset-based welfare approach, which has foremost encouraged homeownership, has led to rising homeownership rates, house prices and household debt levels. While this shift has helped raise the net worth of some among the middle and working classes who own property, the implications for the spatial distribution of wealth in cities have not yet been explored. This paper examines the spatial implications of the rise of policies promoting asset-based welfare, by examining statistically how variables related to homeownership rates and housing prices relate to measures of urban wealth segregation among neighbourhoods. Canadian cites are used as the main case study for the empirical analysis. The findings suggest that while homeownership in general has an equalizing effect, rising rates of homeownership (and to some extent, rising house prices) are associated not with greater spatial equalization and dispersal of wealth, but instead with greater spatial segregation and concentration of wealth within cities.     

A suggested method of estimation for spatial interdependent models with autocorrelated errors,and an application to a county expenditure model

The purpose of this paper is two-fold. First, we describe an estimation procedure that should be useful for spatial models which contain interactions between the dependent variables and autocorrelated error terms. Second, we apply that procedure to a spatial model relating to county police expenditures. Our estimation procedure does not require the specification of the error distribution, and its computational burden is modest. Large sample results are given.     

Implications of patterns of carbon pools and fluxes across a semiarid environmental gradient

Landscape scale environmental gradients present variable spatial patterns and ecological processes caused by climate, topography and soil characteristics and, as such, offer candidate sites to study environmental change. Data are presented on the spatial pattern of dominant species, biomass, and carbon pools and the temporal pattern of fluxes across a transitional zone shifting from Great Basin Desert scrub, up through pinyon–juniper woodlands and into ponderosa pine forest and the ecotones between each vegetation type. The mean annual temperature (MAT) difference across the gradient is approximately 3°C from bottom to top (MAT 8.5–5.5) and annual precipitation averages from 320 to 530 mm/yr, respectively. The stems of the dominant woody vegetation approach a random spatial pattern across the entire gradient, while the canopy cover shows a clustered pattern. The size of the clusters increases with elevation according to available soil moisture which in turn affects available nutrient resources. The total density of woody species declines with increasing soil moisture along the gradient, but total biomass increases. Belowground carbon and nutrient pools change from a heterogenous to a homogenous distribution on either side of the woodlands. Although temperature controls the seasonal patterns of carbon efflux from the soils, soil moisture appears to be the primary driving variable, but response differs underneath the different dominant species. Similarly, decomposition of dominant litter occurs faster at the cooler and more moist sites, but differs within sites due to litter quality of the different species. The spatial pattern of these communities provides information on the direction of future changes. The ecological processes that we documented are not statistically different in the ecotones as compared to the adjoining communities, but are different at sites above the woodland than those below the woodland. We speculate that an increase in MAT will have a major impact on C pools and C sequestering and release processes in these semiarid landscapes. However, the impact will be primarily related to moisture availability rather than direct effects of an increase in temperature.     

基于消费者行为模拟的商业综合体空间优化策略

摘要　商业综合体的既有研究对消费者行为规律的关注不足。旨在从微观视角揭示空间环境对个体活动的影响,通过 对消费者行为的模拟实现商业综合体的空间优化。基于实际的消费者行为个体数据拟合空间行为模型,在此基础上提 出“一般性模拟”的方法,构建一系列对照性的虚拟情景,控制其他要素,只在特定的设计模式和手法上进行比较, 包括主力店与一般店的关系、不同的平面形态和客流动线、竖向网络联系、出入口的分布等。通过个体模拟,比较了 这些具有导则性质的优化策略所导致的消费者行为的差异,为评价其空间绩效提供更加客观的量化依据。     

Comparison of thermal performance between test cells with different coverage systems for experimental typical day of heat in Brazilian Southeastern

This article shows experimentally the thermal performance of two test cells with different coverage systems, Light Green Roof (LGR) and ceramic roof by analyzing internal surface temperatures (IST) in the ceiling and dry bulb temperatures (DBT). The objective was to evaluate the spatial distribution of temperatures in buildings according to spatial and temporal Dynamic Climatology approaches. An experimental, typical day for heat conditions was determined. The data of the main climatic variables provided by an automatic weather station and temperatures inside the test cells were collected using thermocouples installed such that the entire space is included. The results led to the conclusion that the LGR has a balanced IST and DBT spatial distribution compared with ceramic roofs. Nevertheless, the analysis of the thermal performance is only one of the variables that must be considered when developing a construction proposal that is adapted to the context. The manner in which the thermocouples were placed inside the test cells also showed the importance of specifying the location of the sensors in experimental studies on the behavior and thermal performance of buildings.     

On the distance dependence of the price elasticity of telecommunications demand; review, analysis, and alternative theoretical backgrounds

The positive correlation between the absolute price elasticity of telecommunications demand and the distance of the calling relation is often suggested and presented as an empirical regularity. In this paper we first present an overview of existing studies to buttress the distance dependence empirically. A statistical analysis of the results confirms the existence of distance dependence, and gives insight into the size of the effect: doubling the distance leads to an increase of the elasticity of 0.07.  Next we look for various explanations of the distance dependence. We analyze the roles of the functional form of demand functions in conjunction with the dependence of price on distance, and consider whether spatial interaction theory can provide an explanation. One of the interesting findings is that the price effect may explain the distance dependence, but that this explanation is not unequivocal. In addition we show that incorporating spatial interaction theory elements in a quite basic utility maximization model of information demand also leads to distance dependent telecommunications demand. Received: December 1999/Accepted: July 2001     

The impact of street lights on spatial-temporal patterns of crime in Detroit,Michigan

The importance of understanding crime in the United States assumed enhanced protrusion in the wake of the increased crime rates year by year in certain cities. Neighborhood social demographic variables have been largely used to measure their associations with crime. Other than those social factors, street lighting is a feature of urban and suburban settlement which is widely thought to be a necessary element in preventing crime. Previous research has drawn mixed conclusions about the relationship between street lighting and crime, and the effect of streetlights on neighborhood crime is not entirely definitive. To address this challenge, we examined the spatial associations between street light density, neighborhood social disorganization characteristics and crime (e.g., burglary, vehicle theft, weapons offenses, etc.) in Detroit, Michigan in 2014. Using the street lighting data from the Detroit Public Lighting Authority, crime data from the City of Detroit, supplemented with Census 2010 data, we conducted a Generalized Least Squares model of neighborhood crime in 879 census block groups to test the random effects of the spatial variables and different hours of day on crime. The results show an inverse relationship between street light density and crime rates across census block groups in Detroit and the effects of time period of a day vary according to different types of crime. These findings provided more credible evidence for researchers and policy makers to effectively optimize scarce public safety resources, such as improving street lighting in disadvantaged neighborhoods.