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An Index for Measuring Spatial Graph Dispersion in Socio-Economic Networks

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Abstract

Spatial or geographical distance is influential in many socio-economic networks, but its combination with graph theoretical analysis is challenging. In this study, we define a node and network level spatial dispersion index which combines tie strength and spatial distance in a weighted graph to measure average spatial dispersion of socio-economic activities. The index is computed using an average of tie distances weighted with tie strengths. We define weighted vs unweighted, directed vs undirected, and generalized variants of the index. We demonstrate the use of our index to analyse the network of migration flows between provinces of Turkey by (1) comparing the geographic outreach of migration from provinces in different regions, (2) comparing spatial dispersion of migration to different country level spatial networks of flow such as trade, travel, or health services, and (3) testing effects of population and economic development on spatial dispersion of migration. Our results use weighted vs unweighted, and directed vs undirected variants of the index. Since the index is not problem specific, its use not only prove useful in quantifying features of the network in focus but also allows comparison across different networks. Results of this application demonstrate the suitability of the new index in quantifying and comparing the socio-economic activity in geographically dispersed networks and interpreting the differences.

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Data Availibility Statement

The migration datasets analysed during the current study are publicly available in the “statistical data portal” repository, https://data.tuik.gov.tr/. Spatial Dispersion Indices for secondary education, higher education, health service visits, transportation, trade, package shipments, and phone communication in Turkey were published by the Turkish Ministry of Industry and Technology (Gençer et al., 2020), as part of YER-SİS project report, an electronic copy of which is publicly available at https://www.kalkinmakutuphanesi.gov.tr/dokuman/yer-sis-iller-ve-bolgeler-arasi-sosyo-ekonomik-ag-iliskileri-raporu/2591.

References

  • Adams, J., Faust, K., Lovasi, G. S. (2012). Capturing context: Integrating spatial and social network analyses. Social networks, 34(1), ,

  • Anderson, J. E. (2011). The gravity model. Annu. Rev. Econ., 3(1), 133–160.

    Google Scholar 

  • Bachi, R. (1963). Standard distance measures and related methods for spatial analysis. Papers of the regional science association (Vol. 10, pp. 83–132)

  • Badia, H. (2020). Comparison of bus network structures in face of urban dispersion for a ring-radial city. Networks and Spatial Economics, 20(1), 233–271.

    Article  Google Scholar 

  • Barbero, J., & Zofío, J. L. (2016). The multiregional core-periphery model: The role of the spatial topology. Networks and Spatial Economics, 16(2), 469–496.

    Article  MathSciNet  Google Scholar 

  • Barthélemy, M. (2011). Spatial networks. Physics Reports, 499(1–3), 1–101.

    Article  MathSciNet  ADS  Google Scholar 

  • Bruno, G., & Genovese, A. (2012). A spatial interaction model for the representation of the mobility of university students on the italian territory. Networks and Spatial Economics, 12(1), 41–57.

    Article  MathSciNet  Google Scholar 

  • Calabrese, F., Dahlem, D., Gerber, A., Paul, D., Chen, X., Rowland, J., Ratti, C. (2011). The connected states of America: Quantifying social radii of influence. 2011 ieee third international conference on privacy, security, risk and trust and 2011 ieee third international conference on social computing (pp. 223–230)

  • Chandra, S., & Quadrifoglio, L. (2013). Critical street links for demand responsive feeder transit services. Computers & Industrial Engineering, 66(3), 584–592.

    Article  Google Scholar 

  • Chaudey, M., & Bouzid, S. (2021). Location of franchise networks in the united states: What lessons for networks strategies? Applied Spatial Analysis and Policy, 14(4), 755–776.

    Article  Google Scholar 

  • Clemens, M. A. (2014). Does development reduce migration? International handbook on migration and economic development, , 152–185

  • Daraganova, G., Pattison, P., Koskinen, J., Mitchell, B., Bill, A., Watts, M., & Baum, S. (2012). Networks and geography: Modelling community network structures as the outcome of both spatial and network processes. Social Networks, 34(1), 6–17.

    Article  Google Scholar 

  • Daskin, M. S. (2008). What you should know about location modeling. Naval Research Logistics (NRL), 55(4), 283–294.

    Article  MathSciNet  Google Scholar 

  • Desmet, K., Gomes, J. F., & Ortuño-Ortín, I. (2020). The geography of linguistic diversity and the provision of public goods. Journal of Development Economics, 143, 102384.

    Article  Google Scholar 

  • Di Maria, C., & Stryszowski, P. (2009). Migration, human capital accumulation and economic development. Journal of Development Economics, 90(2), 306–313.

    Article  Google Scholar 

  • Droździel, P., Wińska, A., Madleňák, R., Szumski, P. (2017). Optimization of the position of the local distribution centre of the regional post logistics network. Transport Problems, 12, ,

  • Expert, P., Evans, T. S., Blondel, V. D., & Lambiotte, R. (2011). Uncovering space-independent communities in spatial networks. Proceedings of the National Academy of Sciences, 108(19), 7663–7668.

    Article  CAS  ADS  Google Scholar 

  • Gençer, M., Işık, M., Meydan, M. C., Kazancık, L. B., & Ersayın, Z., Saygılı, A., Tek, B. (2020). İller ve bölgeler arası sosyo-ekonomik ağ İlişkileri raporu. Turkish Ministry of Industry and Technology, Development Agency

  • Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. The American Journal Of Sociology, 91(3), 481–510.

    Article  Google Scholar 

  • Gu, H., Meng, X., Shen, T., & Wen, L. (2020). China’s highly educated talents in 2015: Patterns, determinants and spatial spillover effects. Applied Spatial Analysis and Policy, 13, 631–648.

    Article  Google Scholar 

  • Haandrikman, K., van Wissen, L. J., & Harmsen, C. N. (2011). Explaining spatial homogamy. compositional, spatial and regional cultural determinants of regional patterns of spatial homogamy in the Netherlands. Applied Spatial Analysis and Policy, 4, 75–93.

    Article  Google Scholar 

  • Haoran, Y., Cong, W., & Youyang, Y. (2022). The spatial structure evolution of China’s high-speed rail network and its impacts on real estate investment. Applied Spatial Analysis and Policy, 15(1), 49–69.

    Article  Google Scholar 

  • Hasman, J., & Novotnỳ, J. (2018). Uncovering the patterns of the us geography of immigration by an analysis of spatial relatedness between immigrant groups. Applied Spatial Analysis and Policy, 11, 257–286.

  • Hipp, J. R., Faris, R. W., & Boessen, A. (2012). Measuring ‘neighborhood’: Constructing network neighborhoods. Social Networks, 1(34), 128–140.

    Article  Google Scholar 

  • Hodler, R., Valsecchi, M., & Vesperoni, A. (2021). Ethnic geography: Measurement and evidence. Journal of Public Economics, 200, 104446.

    Article  Google Scholar 

  • Levy, M. (2010). Scale-free human migration and the geography of social networks. Physica A: Statistical Mechanics and its Applications, 389(21), 4913–4917.

    Article  ADS  Google Scholar 

  • Levy, M., & Goldenberg, J. (2014). The gravitational law of social interaction. Physica A: Statistical Mechanics and its Applications, 393(C), 418–426

  • Lin, J. (2012). Network analysis of China’s aviation system, statistical and spatial structure. Journal of Transport Geography, 22, 109–117.

    Article  Google Scholar 

  • Myint, S. W. (2008). An exploration of spatial dispersion, pattern, and association of socio-economic functional units in an urban system. Applied Geography, 3(28), 168–188.

    Article  Google Scholar 

  • Okabe, A., Okunuki, K. -i., Shiode, S. (2006). Sanet: a toolbox for spatial analysis on a network. Geographical analysis, 38(1), 57–66.

  • Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. https://doi.org/10.1016/j.socnet.2010.03.006 Retrieved from http://www.sciencedirect.com/science/article/pii/S0378873310000183

  • Patuelli, R., Reggiani, A., Gorman, S. P., Nijkamp, P., & Bade, F.-J. (2007). Network analysis of commuting flows: A comparative static approach to German data. Networks and Spatial Economics, 7(4), 315–331.

    Article  Google Scholar 

  • Preciado, P., Snijders, T. A., Burk, W. J., Stattin, H., & Kerr, M. (2012). Does proximity matter? distance dependence of adolescent friendships. Social Networks, 1(34), 18–31.

    Google Scholar 

  • Salvati, L. (2020). Residential mobility and the local context: Comparing long-term and short-term spatial trends of population movements in Greece. Socio-Economic Planning Sciences, 72, 100910.

    Article  Google Scholar 

  • Schmutz, B., & Sidibé, M. (2018). Frictional labour mobility. The Review of Economic Studies, 86(4), 1779–1826. https://doi.org/10.1093/restud/rdy056 Retrieved from https://academic.oup.com/restud/article-pdf/86/4/1779/28883147/rdy056.pdf

  • Scott, L. M., & Janikas, M. V. (2010). Spatial statistics in arcgis. Handbook of applied spatial analysis (pp. 27–41). Springer

  • Sojahrood, Z. B., & Taleai, M. (2021). A poi group recommendation method in location-based social networks based on user influence. Expert Systems with Applications, 171, 114593.

    Article  Google Scholar 

  • Vani, G., & Maoh, H. (2023). Characterizing the nature of a multi-regional trucking network using the network robustness index: An application to Ontario, Canada. Applied Spatial Analysis and Policy, 16(1), 383–407.

  • Viry, G. (2012). Residential mobility and the spatial dispersion of personal networks: Effects on social support. Social Networks, 34(1), 59–72.

    Article  MathSciNet  Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social Network Analysis. Cambridge

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Correspondence to Mehmet Gençer.

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Gençer, M. An Index for Measuring Spatial Graph Dispersion in Socio-Economic Networks. Appl. Spatial Analysis 17, 323–343 (2024). https://doi.org/10.1007/s12061-023-09545-8

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