A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels

The daily trip to work is ubiquitous, yet its characteristics differ widely from person to person and place to place. This is manifested in statistics on mode and distance of travel, which vary depending on a range of factors that operate at different scales. This heterogeneity is problematic for decision makers tasked with encouraging more sustainable commuter patterns. Numerical models, based on real commuting data, have great potential to aid the decision making process. However, we contend that new approaches are needed to advance knowledge about the social and geographical factors that relate to the diversity of commuter patterns, if policies targeted to specific individuals or places are to be effective. To this end, the paper presents a spatial microsimulation approach, which combines individual-level survey data with geographically aggregated census results to tackle the problem. This method overcomes the limitations imposed by the lack of available geocoded micro-data. Further, it allows a range of scales of analysis to be pursued in parallel and provides insights into both the types of area and individual that would benefit most from specific interventions.     

Modeling regional economic dynamics: Spatial dependence,spatial heterogeneity and nonlinearities

Spatial modeling of economic phenomena requires the adoption of complex econometric tools, which allow us to deal with important methodological issues, such as spatial dependence, spatial unobserved heterogeneity and nonlinearities. In this paper we describe some recently developed econometric approaches (i.e. Spatial Autoregressive Semiparametric Geoadditive Models), which address the three issues simultaneously. We also illustrate the relative performance of these methods with an application to the case of house prices in the Lucas County.     

Financial openness and Chinese regional growth imbalance: New insight from spatial spillovers

This paper uses spatial panel methods and Chinese provincial data from 2003 to 2017 to study the spatial spillovers of financial openness on economic growth. The results show, first, a positive direct effect and an overall negative spatial spillover of financial openness on provincial growth. Second, there are two spatial spillover channels: a positive growth externality and a harmful resource competition among provinces. Third, we estimate the state dependence and dynamics of spatial spillover, and find that the negative spatial spillover is smaller in provinces with high levels of financial openness and in the long term; thus, the negative spatial spillover declined over time. These results are robust to the choice of SDM and GNS spatial econometrics methods and under different spatial weight matrices.     

经济发展方式转型与经济软实力空间分布关联研究——以河南18个省辖市区域为例

经济软实力是决定经济发展方式转型的关键因子,经济软、硬实力的不同配置决定经济发展方式类型。经济软实力不仅内在具有推动经济发展方式转型的能力,而且还具有实现其转型的内在机理。通过河南省区域经济软实力空间分布及其与区域发展指数关联的实证分析,证明以提升经济软实力促进区域经济发展方式转变是经济发展的一种必然规律。     

空间分析在住宅价值评估中应用研究综述

住宅是一种异质性商品，它的构成使用价值的各个特征之间有明显的差异，因此特征价格定价模型广泛用于住宅的价值评估。但是由于住宅数据中常常表现出空间问题，使得传统的特征价格模型不再适用，空间效应在不动产领域的存在引起了学者们的广泛关注。对空间分析在住宅价值评估中的应用研究进行了回顾和阐述，首先分析空间效应的来源和影响，讨论了在传统模型中纳入空间效应的重要性，接着阐述如何对这些空间效应进行有效建模和检验，并分析了进一步研究的方向。     

Do housing prices promote total factor productivity? Evidence from spatial panel data models in explaining the mediating role of population density

The issue of what to promote in total factor productivity (TFP) in urban areas has been widely discussed in academia and housing prices and population density are confirmed to be two of the most essential driving factors. However, research into the interaction of housing prices and population density with TFP has been neglected, with no previous studies taking spatial factors into consideration, which may bias the results. From this perspective, using spatial panel data models and employing instrumental variables to solve the endogenous problem, this study examines the impact of housing prices on TFP through the mediating effect of population density for 283 Chinese cities during the period 2000–2013, and confirms that the mediating effect accounts for 18.70 % of the total effect. The results show the positive and significant association of housing prices with TFP and the inverted U-shape of population density. The underlying logic is that housing prices change population density by attracting people with high purchasing power and discouraging those unable to afford housing, whereas increased density helps to promote productivity since the settled inhabitants always have highly developed work skills and are well educated. The influencing mechanism of housing prices on TFP through population density is analyzed, namely the spillover effect. We find that the spillover effect exists in the eastern and central regions, as well as first, second, and third tier cities, while for western regions and fifth tier cities, population mobility and increased in housing prices slows their economic development. There is no evidence of any spillover effect in fourth tier cities. A discussion and suggested policy implications are also provided.     

Do your neighbors affect your bicycling choice? A spatial probit model for bicycling to The Ohio State University

Neighborhood social effects have recently become a focus of interest in transportation research, whereby transportation mode choice is not only affected by an individual’s characteristics and transportation system conditions, but also by the mode choices of that individual’s social neighbors. This study supports the neighborhood social effects argument, using a spatial econometrics approach and data from The Ohio State University (OSU) 2012 Campus Transportation Survey. A spatial probit model of commuters’ mode choices (bicycling versus non-bicycling) is estimated, accounting for spatial autocorrelation. The results show that the more OSU-affiliated bicycle riders are residing around an individual OSU commuter, the more attractive bicycling becomes, controlling for other factors such as gender, status, proximity to campus, bicycle infrastructure and attitudes. The results indicate that students and males are more likely to commute by bicycles. The probability of choosing bicycles decreases with distance from campus. In addition, proximity to bicycle infrastructure and physical environment both encourage respondents to bicycle. Feeling of safety, travel cost and concern for the environment also affect bicycling choice.     

Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.     

Forecasting using heterogeneous panels with cross-sectional dependence

In this paper, we focus on forecasting methods that use heterogeneous panels in the presence of cross-sectional dependence in terms of both spatial error dependence and common factors. We propose two main approaches to estimating the factor structure: a residuals-based approach, and an approach that uses a panel of auxiliary variables to extract the factors. Small sample properties of the proposed methods are investigated through Monte Carlo simulations and applied to predict house price inflation in OECD countries.     

The geography of suppliers and retailers

In the last decades, supply chains have increasingly transcended national boundaries developing into global supply chains. Along with the many opportunities arising from international sourcing and the extended commercial presence over the world, the management of a globally dispersed supply chain is highly complex. A key issue to consider when dealing with the global supply chain design is the location of facilities, not only with respect to firms' owned facilities but also the supply and distribution side as factors that affect supply chain complexity and operational performance. This paper sets out a methodological framework to characterize the geographical configuration of a firm's suppliers and retailer networks. Quantitative indexes of network spatial concentration and relative proximity measures based on a nonparametric kernel density estimator are developed to identify both intra- and inter-firm patterns between the supply and point of sales' distributions. The method is first described by means of a series of theoretical-illustrative examples and exemplified by analyzing the geographical dispersion of four practical cases from the fashion-textile industry (i.e., Adidas, Benetton, C&A, and Puma). Subsequently, managerial implications and potential use of the metrics are discussed, showing how the proposed approach can support researchers and practitioners to improve supply chain location decisions and logistic integration, and evaluate changes in either the purchasing or distribution strategy.