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Luc E. Anselin (born December 1, 1953) is one of the developers of the field of spatial econometrics.
Luc Anselin was previously the Regents' Professor, Walter Isard Chair and Director of the School of Geographical Sciences and Urban Planning at Arizona State University (ASU) where he attracted some of the leading spatial econometrics scholars. He also founded and directed the GeoDa Center for Geospatial Analysis and Computation at ASU to develop, implement, apply, and disseminate spatial analysis methods. In 2016, the GeoDa Center for Geospatial Analysis relocated to the University of Chicago. [1] He held prior appointments at the University of Illinois, Urbana-Champaign, University of Texas at Dallas, West Virginia University, the University of California, Santa Barbara and the Ohio State University. His joint appointments included a range of disciplines, including Geography, Urban and Regional Planning, Economics, Agricultural and Consumer Economics, Political Economy and Political Science.
In recent years,[ when? ] several national and international awards recognized Anselin's achievements, including his development of new spatial methodologies (e.g., local indicators of statistical association) and spatial software tools. The Regional Science Association International elected him as Fellow in 2004, and awarded him their Walter Isard Prize in 2005 and their William Alonso Memorial Prize in 2006. In 2008, Anselin was awarded one of the nation's highest academic honors by being elected to the National Academy of Sciences as well as to the American Academy of Arts and Sciences. In 2012, Anselin was elected as a University Consortium for Geographic Information Science Fellow (UCGIS).
2008 marked the 20th anniversary of the book Spatial Econometrics: Methods and Models that Anselin is best known for and that has been cited over 6,000 times. One of Anselin's achievements has been his contributions to moving the discipline of spatial econometrics from the margins in 1988 to current acceptance in mainstream econometrics, thereby advancing the econometric foundations of Geographic Information Science. His publications include several hundred articles and edited books (including New Directions in Spatial Econometrics in 1995 and Advances in Spatial Econometricsmin 2004) in the fields of Quantitative Geography, Regional Science, GIScience, Econometrics, Economics and Computer Science.
His development of spatial software further facilitated the establishment of spatial econometrics. Software tools include SpaceStat (spatial econometrics), GeoDa (exploratory spatial data analysis and spatial regression modeling), and collaborative efforts such as PySAL, an open source library of spatial analytic functions based on the Python programming language. GeoDa had over 56,000 users within six years of its creation.
A native of Belgium, Anselin graduated magna cum laude with a B.S. in economics in 1975 and summa cum laude with an M.S. in Statistics, Econometrics and Operations in 1976, both from the Vrije Universiteit Brussel. Around this time, the origins of spatial econometrics began to take shape in economics departments in the Netherlands and geography/regional science departments in the UK. In 1977, he moved from Belgium to the U.S. to enroll in Cornell University's interdisciplinary doctoral program in regional science. This provided the opportunity to work with Walter Isard and William Greene. He earned his doctorate in regional science in 1980.
A geographic information system (GIS) consists of integrated computer hardware and software that store, manage, analyze, edit, output, and visualize geographic data. Much of this often happens within a spatial database; however, this is not essential to meet the definition of a GIS. In a broader sense, one may consider such a system also to include human users and support staff, procedures and workflows, the body of knowledge of relevant concepts and methods, and institutional organizations.
Regional science is a field of the social sciences concerned with analytical approaches to problems that are specifically urban, rural, or regional. Topics in regional science include, but are not limited to location theory or spatial economics, location modeling, transportation, migration analysis, land use and urban development, interindustry analysis, environmental and ecological analysis, resource management, urban and regional policy analysis, geographical information systems, and spatial data analysis. In the broadest sense, any social science analysis that has a spatial dimension is embraced by regional scientists.
Geomatics is defined in the ISO/TC 211 series of standards as the "discipline concerned with the collection, distribution, storage, analysis, processing, presentation of geographic data or geographic information". Under another definition, it consists of products, services and tools involved in the collection, integration and management of geographic (geospatial) data. Surveying engineering was the widely used name for geomatic(s) engineering in the past. Geomatics was placed by the UNESCO Encyclopedia of Life Support Systems under the branch of technical geography.
A GIS software program is a computer program to support the use of a geographic information system, providing the ability to create, store, manage, query, analyze, and visualize geographic data, that is, data representing phenomena for which location is important. The GIS software industry encompasses a broad range of commercial and open-source products that provide some or all of these capabilities within various information technology architectures.
Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.
GeoDa is a free software package that conducts spatial data analysis, geovisualization, spatial autocorrelation and spatial modeling.
Indicators of spatial association are statistics that evaluate the existence of clusters in the spatial arrangement of a given variable. For instance, if we are studying cancer rates among census tracts in a given city local clusters in the rates mean that there are areas that have higher or lower rates than is to be expected by chance alone; that is, the values occurring are above or below those of a random distribution in space.
Geary's C is a measure of spatial autocorrelation that attempts to determine if observations of the same variable are spatially autocorrelated globally. Spatial autocorrelation is more complex than autocorrelation because the correlation is multi-dimensional and bi-directional.
In statistics, Moran's I is a measure of spatial autocorrelation developed by Patrick Alfred Pierce Moran. Spatial autocorrelation is characterized by a correlation in a signal among nearby locations in space. Spatial autocorrelation is more complex than one-dimensional autocorrelation because spatial correlation is multi-dimensional and multi-directional.
Spatial econometrics is the field where spatial analysis and econometrics intersect. The term “spatial econometrics” was introduced for the first time by the Belgian economist Jean Paelinck in the general address he delivered to the annual meeting of the Dutch Statistical Association in May 1974 . In general, econometrics differs from other branches of statistics in focusing on theoretical models, whose parameters are estimated using regression analysis. Spatial econometrics is a refinement of this, where either the theoretical model involves interactions between different entities, or the data observations are not truly independent. Thus, models incorporating spatial auto-correlation or neighborhood effects can be estimated using spatial econometric methods. Such models are common in regional science, real estate economics, education economics, housing market and many others. Adopting a more general view, in the by-law of the Spatial Econometrics Association, the discipline is defined as the set of “models and theoretical instruments of spatial statistics and spatial data analysis to analyse various economic effects such as externalities, interactions, spatial concentration and many others”. Recent developments tend to include also methods and models from social network econometrics.
CrimeStat is a crime mapping software program. CrimeStat is Windows-based program that conducts spatial and statistical analysis and is designed to interface with a geographic information system (GIS). The program is developed by Ned Levine & Associates under the direction of Ned Levine, with funding by the National Institute of Justice (NIJ), an agency of the United States Department of Justice. The program and manual are distributed for free by NIJ.
Anil K. Bera is an Indian-American econometrician. He is Professor of Economics at University of Illinois at Urbana–Champaign's Department of Economics. He is most noted for his work with Carlos Jarque on the Jarque–Bera test.
The Regional Research Institute (RRI) at West Virginia University is a university-wide regional science research center for graduate students and faculty members in the fields of economics, resource economics, geography, history and sociology. Professor William H. Miernyk, a regional economist trained at Harvard, came to West Virginia University and founded RRI and served as the 1st Director. Since its opening in 1965, the Regional Research Institute has helped scholars do research. For numerous individuals, both at West Virginia University and elsewhere, it has provided crucial encouragement, stimulation, and opportunities. Its programs involve faculty members, graduate students, and an extensive network of scholars in the United States and abroad.
Manfred M. Fischer is an Austrian and German regional scientist, Emeritus Professor of economic geography at the WU-Vienna University of Economics and Business, and adjunct professor at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences in Beijing.
Roger Simon Bivand is a British geographer, economist and professor at the Norwegian School of Economics. He specialises in open source software for spatial analysis, and played a major role in developing functions for spatial data in the R statistical programming language, including the R packages sp, rgdal, maptools and rgrass7. His book Applied Spatial Data Analysis with R (2008), coauthored with Edzer Pebesma and Virgilio Gómez-Rubio, is considered "the authoritative resource on R's spatial capabilities".
Giuseppe Arbia is an Italian statistician. He is known for his contributions to the field of spatial statistics and spatial econometrics. In 2006 together with Jean Paelinck he founded the Spatial Econometrics Association, which he has been chairing ever since.
Spatial neural networks (SNNs) constitute a supercategory of tailored neural networks (NNs) for representing and predicting geographic phenomena. They generally improve both the statistical accuracy and reliability of the a-spatial/classic NNs whenever they handle geo-spatial datasets, and also of the other spatial (statistical) models whenever the geo-spatial datasets' variables depict non-linear relations.
Arthur Getis was an American geographer known for his significant contributions to spatial statistics and geographic information science (GIScience). With a career spanning over four decades, Getis authored more than one hundred peer-reviewed papers and book chapters, greatly influencing GIScience and geography as a whole. The Getis-Ord family of statistics, one of the most commonly used in spatial analysis, is based on his and J. Keith Ord's work and is still widely used in the creation of hot spot maps.
Join count statistics are a method of spatial analysis used to assess the degree of association, in particular the autocorrelation, of categorical variables distributed over a spatial map. They were originally introduced by Australian statistician P. A. P. Moran. Join count statistics have found widespread use in econometrics, remote sensing and ecology. Join count statistics can be computed in a number of software packages including PASSaGE, GeoDA, PySAL and spdep.
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