Alternative spatial weights matrices: methodology and application in calculating LISA
DOI:
https://doi.org/10.21638/spbu07.2023.210Abstract
The heuristic potential of spatial methods depends heavily on thechoice ofa spatial weight matrix. The article illustrates the creation and use ofabsolute geographic versus relative socio-political matrix and tests both by calculating spatial autocorrelation for development indicators. Particularly, a comparison is made of spatial association patterns in geometric and geopolitical space on a sample of the 193 UN member states. The Moran's I is calculated to assess the degree of the neighborhood effect for both matrices. International clustering patterns are explored with local indicators of spatial association (LISA), plotted on a map with the two types of matrices. Comparing LISA cartograms helps identify possible opportunities for socio-political phenomena to spread throughout space, as well as the trends inthe spatial organization of the international politics. Carrying out calculations with different matrices allows us to single out groups of observations that constitute the core of the cluster. On the contrary, “transitive” observations that change their cluster affiliation in a supposedly homogeneous group, can also be detected. Also, the usage of different types of weight matrices can help highlight the “gray zones” - parts of the data set that lack spatial autocorrelation and may require additional research. Overall, the results suggest that usingboth absolute topological and relative socio-economic weight matrices is reasonable for the purposes of exploratory spatial analysis. Using matrices based on different types of variables concurrently can assist in detecting new trends in spatial organization and providing empirical confirmation for existing spatial patterns.
Keywords:
spatial weights matrix, relative neighbourhood, local indicators of spatial association, spatial autocorrelation, political geography, system of international relations, geopolitical bloc
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References
Балаш, О. С. (2013). Пространственный анализ темпов роста городов России. Известия Саратовского университета. Новая серия. Серия Экономика. Управление. Право, 13 (2), 186–191.
Вакуленко, Е. С. (2015). Анализ связи между региональными рынками труда в России с использованием модели Оукена. Прикладная эконометрика, 4 (40), 28–48.
Захарова, Е. А. (2021). Электоральные процессы в фюльке Норвегии через призму пространственного анализа. Псковский регионологический журнал, 1 (45), 110–125.
Латков, А. В., Никифорова, Е. В., Толмачев, М. Н., Барашов, Н. Г. (2020). Динамика показателей дифференциации заработной платы в экономическом пространстве России в условиях макроэкономической нестабильности. Балканское научное обозрение, 3 (9), 90–93.
Михеева, Н. Н. (2016). Исходные условия для формирования новой модели экономического роста: пространственный аспект. Научные труды: Институт народнохозяйственного прогнозирования РАН, 1, 586–605.
Нефедова, Т. Г., Трейвиш, А. И., Шелудков, А. В. (2022). Полимасштабный подход к выявлению пространственного неравенства в России как стимула и тормоза развития. Известия Российской академии наук. Серия географическая, 3, 289–303.
Окунев, И. Ю. (2023). Основы пространственного анализа. 2-е изд., перераб. и доп. М.: Аспект Пресс.
Семерикова, Е. В. и Демидова, О. А. (2015). Анализ региональной безработицы в России и Германии: пространственно-эконометрический подход. Пространственная экономика, 2, 64–85. https://doi.org/10.14530/se.2015.2.064-085
Торкунов, А. В. (2012). Политические системы современных государств: энциклопедический справочник. В 4 т. М.: Аспект Пресс.
Agnew, J. (1987). Place and Politics: The Geographical Mediation of State and Society, 1st ed. London: Routledge.
Agnew, J. (1996). Mapping politics: how context counts in electoral geography. Political Geography, 15 (2), 129–146.
Agnew, J., Mitchell, K., Toal, G. (2003). A Companion to Political Geography. UK: Blackwell Publishing Ltd.
Aldenderfer, M. (1996). Introduction. In: M. Aldenderfer and H. D. G. Maschner, ed., Anthropology, Space, and Geographic Information Systems. New York: Oxford University Press, 1–18.
Anselin, L. (1995). Local Indicators of Spatial Association LISA. Geographical Analysis, 27, 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin, L. (2003). Spatial Externalities, Spatial Multipliers, And Spatial Econometrics. International Regional Science Review, 26 (2), 153–166. https://doi.org/10.1177/0160017602250972
Anselin, L. (2013). Spatial Econometrics: Methods and Models. Netherlands: Springer Netherlands.
Anselin, L. and Rey, S. J. (2010). Perspectives on Spatial Data Analysis. Berlin: Springer-Verlag.
Anselin L., Sridharan S., Gholston S. (2007). Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns. Social Indicators Research, 82 (2), 287–309.
Blanchard, P. and Volchenkov, D. (2009). Mathematical Analysis of Urban Spatial Networks. Berlin; Heidelberg: Springer.
Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, F., Mood, A. M., Weinfeld, F. D., York R. L. (1966). Equality of educational opportunity. Washington, DC: U.S. Government Printing Office.
Cressie, N. (1993). Statistics for Spatial Data. New York: John Wiley & Sons.
Earnest, A., Morgan, G., Mengersen, K., Ryan, L., Summerhayes, R., Beard, J. (2007). Evaluating the effect of neighbourhood weight matrices on smoothing properties of Conditional Autoregressive (CAR) models. International Journal of Health Geographics, 6 (1), 54–65. https://doi.org/10.1186/1476-072x-6-54
Eff, A. (2008). Weight Matrices for Cultural Proximity: Deriving Weights from a Language Phylogeny. Structure and Dynamics. E-Journal of Anthropological and Related Sciences, 3 (2), art. 9.
Elliott, P. and Wartenberg, D. (2004). Spatial epidemiology: current approaches and future challenges. Environmental health perspectives, 112 (9), 998–1006. https://doi.org/10.1289/ehp.6735
Fischer, M. M. and Getis, A. (2010). Handbook of applied spatial analysis: software tools, methods and applications. Berlin: Springer.
Fotheringham, S., Charlton, M., Brunsdon, C. (1998). Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis. Environment and Planning A, 30, 1905–1927. http://dx.doi.org/10.1068/a301905
Fotheringham, A. S. and Rogerson, P. A. (2009). The SAGE handbook of spatial analysis. London; Los Angeles: SAGE Publications, Ltd.
Geary, R. C. (1954). The Contiguity Ratio and Statistical Mapping. The Incorporporated Statistician, 5, 115–145. https://doi.org/10.2307/2986645
Getis, A. and Aldstadt, J. (2004). Constructing the Spatial Weights Matrix Using a Local Statistic. Geographical Analysis, 36 (2), 90–104. https://doi.org/10.1111/j.1538-4632.2004.tb01127.x
Getis, A. and Ord, J. K.(1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24, 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Grattet, R., Jenness, V., Curry, T. (1998). The Homogenization and Differentiation of Hate Crime Law in the United States, 1978 to 1995: Innovation and Diffusion in the Criminalization of Bigotry. American Sociological Review, 63 (2), 286–307. https://doi.org/10.2307/2657328
Grekousis, G. (2020). Spatial Analysis Methods and Practice: Describe — Explore — Explain through GIS. Cambridge: Cambridge University Press.
Johnston, R. J. (1991). A question of place: exploring the practice of human geography. Oxford: Basil Blackwell.
Kuo, T.-M., Lee, R. M., Anselin, L. (2011). Geographic disparities in late-stage breast cancer diagnosis in California. Health & Place, 17 (1), 327–334. https://doi.org/10.1016/j.healthplace.2010.11.007
Lam, C. and Souza, P. C. L. (2019). Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model. Journal of Business & Economic Statistics, 3, 693–710. https://doi.org/10.1080/07350015.2019.1569526
Leenders, R. T. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24 (1), 21–47. https://doi.org/10.1016/S0378-8733(01)00049-1
LeSage, J. and Pace, R. K. (2009). Introduction to Spatial Econometrics. United Kingdom: CRC Press.
Mears, D. P. and Bhati, A. S. (2006). No community is an island: the effects of resource deprivation on urban violence in spatially and socially proximate communities. Criminology, 44 (3), 509–548. https://doi.org/10.1111/j.1745-9125.2006.00056.x
Moran, P. (1948). The Interpretation of Statistical Maps. Journal of the Royal Statistical Society, 10, 243–251.
Morenoff, J. D. (2003). Neighborhood Mechanisms and the Spatial Dynamics of Birth Weight. American Journal of Sociology, 108 (5), 976–1017.
Morenoff, J. D., Sampson, R. J., Raudenbush, S. W. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39 (3), 517–558.
O’Loughlin, J. and Anselin, L. (1991). Bringing geography back to the study of international relations: spatial dependence and regional context in Africa, 1966-1978. International Interactions, 17, 29–61.
O’Loughlin, J., Flint, C., Anselin, L. (1994). The Geography of the Nazi Vote: Context, Confession, and Class in the Reichstag Election of 1930. Annals, Association of American Geographers, 84, 351–380.
O’Loughlin, J., Ward, M., Lofdahl, C., Cohen, J., Brown, D., Reilly, D., Gleditsch, K., Shin, M. (1998). The spatial and temporal diffusion of democracy, 1946-1994. Annals, Association of American Geographers, 88, 545–574.
Rogers, E. M.(1983). Diffusion of Innovations. New York: Free Press.
Sampson, R. J., Morenoff, J. D., Earls, F. (1999). Beyond Social Capital: Spatial Dynamics of Collective Efficacy for Children. American Sociological Review, 64, 633–660.
Tita, G. E. and Greenbaum, R. (2008). Crime, neighborhoods and units of analysis: Putting space in its place. In: D. Weisburd, W. Bernasco, G. J. N. Bruinsma, eds, Putting crime in its place: Units of analysis in spatial crime research. New York: Springer, 145–170.
Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234–240.
Voss, P. R., White, K. J. C., Hammer, R. B. (2006). Explorations in Spatial Demography. In: W. A. Kandel and D. L. Brown, eds, Population Change and Rural Society. Dordrecht: Springer, 407–429.
Waller, L. A., Carlin, B. P., Xia, H., Gelfand, A. E. (1997). Hierarchical Spatio-Temporal Mapping of Disease Rates. Journal of the American Statistical Association, 92, 607–617.
Wang, Y., Kockelman, K., Murray, W. (2013). The impact of weight matrices on parameter estimation and inference: A case study of binary response using land-use data. Journal of Transport and Land Use, 6 (3), 75–85.
Weisburd, D. and McEwen, T. (1998). Crime Mapping and Crime Prevention. New York: Criminal Justice Press.
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Published
2023-05-11
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Okunev, I. Y. and Kushnareva, A. E. (2023) “Alternative spatial weights matrices: methodology and application in calculating LISA”, Vestnik of Saint Petersburg University. Earth Sciences, 68(2). doi: 10.21638/spbu07.2023.210.
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Articles of "Vestnik of Saint Petersburg University. Earth Sciences" are open access distributed under the terms of the License Agreement with Saint Petersburg State University, which permits to the authors unrestricted distribution and self-archiving free of charge.
