Thirty Years of Spatial Econometrics

In this paper, I give a personal view on the development of the field of spatial econometrics during the past thirty years. I argue that it has moved from the margins to the mainstream of applied econometrics and social science methodology. I distinguish three broad phases in the development, which I refer to as preconditions, takeoff and maturity. For each of these phases I describe the main methodological focus and list major contributions. I conclude with some speculations about future directions.

From SpaceStat to CyberGIS: Twenty Years of Spatial Data Analysis Software

This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.

Is the Price Right? Assessing Estimates of Cadastral Values for Bogotá, Colombia

Hedonic house price models are increasingly applied in the process of mass appraisal, in which econometric specifications are used to obtain automated valuation of properties for taxation purposes. The predictive quality of such models is important, since it directly affects the revenue stream of local authorities. In this paper, we assess the relative predictive performance of different model specifications used in automated valuation. Specifically, we focus on the issue of spatial heterogeneity by comparing models that utilize different definitions of housing submarkets. In addition, we consider the inclusion of “spatial†explanatory variables in the form of distance to various amenities as computed from a GIS. We apply this to data from the city of Bogot Ìa, Colombia, a pioneer in the application of mass appraisal techniques in a developing country context. We find that specifications that include the submarkets improve predictive performance and that the inclusion of the spatial variables is superior to the traditional models of homogenous zones. However, even the best models are still characterized by relatively poor performance in the form of a high degree of overprediction of the house value. In addition, the predictive performance of the models varied by socio-economic stratum in the city, which suggests that the dynamics of the housing markets in these strata would require closer and separate attention. These results may provide further guidance to enhance mass appraisal practice in the city of Bogot Ìa as well as potentially other Latin American cities.

Spatial Fixed Effects and Spatial Dependence

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification re- moves spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effects may be spurious when the true DGP takes the form of a spatial lag or spatial error dependence. In addition, we also show that only in the special case where the dependence is group-wise, with all observations in the same group as neighbors of each other, do spatial fixed effects correctly remove spatial correlation.spatial autocorrelation, spatial econometrics, spatial externalities, spatial fixed effects, spatial interaction, spatial weights

The Importance of Broadband Provision to Knowledge Intensive Firm Location

Despite the volume of literature afforded knowledge work and innovations in information and communications technologies (ICTs), few studies have examined the importance of ICTs to firms in knowledge industries. This study will develop spatial econometric models to examine the relative importance of the level of broadband provision to knowledge intensive firms in select U.S.  metropolitan statistical areas (MSAs). Results demonstrate the need for both a spatial econometric and a metropolitan area specific evaluation of this relationship. They also suggest potential spillover effects to knowledge intensive firm location, which may explain why some regional economies are relatively more successful at stimulating firm growth in this increasingly important sector of the U.S economy.

Digital neighborhoods

With the advent of ‘big data’ there is an increased interest in using social media to describe city dynamics. This paper employs geo-located social media data to identify ‘digital neighborhoods’ – those areas in the city where social media is used more often. Starting with geo-located Twitter and Foursquare data for the New York City region in 2014, we applied spatial clustering techniques to detect significant groupings or ‘neighborhoods’ where social media use is high or low. The results show that beyond the business districts, digital neighborhoods occur in communities undergoing shifting socio-demographics. Neighborhoods that are not digitally oriented tend to have higher proportion of minorities and lower incomes, highlighting a social–economic divide in how social media is used in the city. Understanding the differences in these neighborhoods can help city planners interested in generating economic development proposals, civic engagement strategies, and urban design ideas that target these areas

Valuing access to water - a spatial hedonic approach applied to Indian cities

An important infrastructure policy issue for rapidly growing cities in developing countries is how to raise fiscal revenues to finance basic services in a fair and efficient manner. This paper applies hedonic analysis that explicitly accounts for spatial spillovers to derive the value of improved access to water in the Indian cities of Bhopal and Bangalore. The findings suggest that by looking at individual or private benefits only, the analysis may underestimate the overall social welfare from investing in service supply especially among the poorest residents. The paper further demonstrates how policy simulations based on these estimates help prioritize spatial targeting of interventions according to efficiency and equity criteria.Town Water Supply and Sanitation,Housing&Human Habitats,Water Supply and Sanitation Governance and Institutions,Water and Industry,Water Use

Dynamic Manipulation of Spatial Weights Using Web Services

Spatial analytical tools are mostly provided in a desktop environment, which tends to restrict user access to the tools. This project intends to exploit up-to-date web technologies to extend user accessibility to spatial analytic tools. The first step is to develop web services for widely used spatial analysis such as spatial weights manipulation and provide easy-to-use web-based user interface to the services. Users can create, transform, and convert spatial weights for their data sets on web browsers without installing any specialized software.

Properties of tests for spatial error components

In spatial econometrics, the typical alternative of spatial autocorrelation is expressed in the form of a spatial autorregressive process. While the bulk of the literature is devoted to specification tests and estimation methods for these models, alternatives have been suggested as well. In this paper, we consider alternatives that take the form of the spatial error components formulation proposed by Kelejian and Robinson. We consider a number of specification tests against this alternative, based on both a maximum likelihood framework as well as on a general method of moments estimation approach. We compare the performance of these tests in a series of Monte Carlo simulation experiments against a wide range of alternatives of spatial autocorrelation, under a number of different error distributions

Measuring Spatial Dynamics in Metropolitan Areas

This paper introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods†a priori and then studying how resident attributes change over time, our approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both as- pects of a neighborhood transform from one period to the next. Our approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. We also develop indicators of spatial change at both the macro (city) level as well as local (neighborhood) scale. We illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the US for the period 1990-2000.