A Scalable Analytical Framework for Spatio-Temporal Analysis of Neighborhood Change: A Sequence Analysis Approach

  • Nikos PatiasEmail author
  • Francisco Rowe
  • Stefano Cavazzi
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Spatio-temporal changes reflect the complexity and evolution of demographic and socio-economic processes. Changes in the spatial distribution of population and consumer demand at urban and rural areas are expected to trigger changes in future housing and infrastructure needs. This paper presents a scalable analytical framework for understanding spatio-temporal population change, using a sequence analysis approach. This paper uses gridded cell Census data for Great Britain from 1971 to 2011 with 10-year intervals, creating neighborhood typologies for each Census year. These typologies are then used to analyze transitions of grid cells between different types of neighborhoods and define representative trajectories of neighborhood change. The results reveal seven prevalent trajectories of neighborhood change across Great Britain, identifying neighborhoods which have experienced stable, upward and downward pathways through the national socioeconomic hierarchy over the last four decades.


Neighborhood change Sequence analysis Spatio-temporal data analysis Classification Population dynamics 


  1. Abbott A (1983) Sequences of social events: concepts and methods for the analysis of order in social processes. In: Abbott A (ed) Historical methods; Fall 1983; 16, 4; Periodicals Archive Online pg. 129’CrossRefGoogle Scholar
  2. Aghabozorgi S, Seyed Shirkhorshidi A, Ying Wah T (2015) Time-series clustering—a decade review. Inf Syst 53:16–38. Scholar
  3. An L et al (2015) Space–time analysis: concepts, quantitative methods, and future directions. Ann Assoc Am Geogr 105(5):891–914. Scholar
  4. Arribas-Bel D, Tranos E (2018) Characterizing the spatial structure(s) of cities “on the fly”: the space-time calendar. Geogr Anal 50(2):162–181. Scholar
  5. Backman M, Lopez E, Rowe F (2018) Career trajectories and outcomes of forced migrants in Sweden: self-employment, employment or persistent inactivity? Small Bus EconGoogle Scholar
  6. Casado-Díaz JM, Martínez-Bernabéu L, Rowe F (2017) An evolutionary approach to the delimitation of labour market areas: an empirical application for Chile. Spat Econ Anal 12(4):379–403. Scholar
  7. Delmelle EC (2016) Mapping the DNA of urban neighborhoods: clustering longitudinal sequences of neighborhood socioeconomic change. Ann Am Assoc Geogr 106(1):36–56. Scholar
  8. Delmelle EC (2017) Differentiating pathways of neighborhood change in 50 U.S. metropolitan areas. Environ Plan A 49(10):2402–2424. Scholar
  9. Fotheringham AS, Wong DWS (1991) The modifiable areal unit problem in multivariate statistical analysis. Environ Plan A 23(7):1025–1044. Scholar
  10. Gabadinho A et al (2009) Mining sequence data in R with the TraMineR package: a user’s guide for version 1. 1 1’, 1, pp 1–129Google Scholar
  11. Gentle JE, Kaufman L, Rousseuw PJ (1991) Finding groups in data: an introduction to cluster analysis. Biometrics. Scholar
  12. Goodchild MF (2013) Prospects for a space-time GIS. Ann Assoc Am Geogr 103(5):1072–1077. Scholar
  13. Green MA et al (2017) Could the rise in mortality rates since 2015 be explained by changes in the number of delayed discharges of NHS patients? J Epidemiol Community Health 71(11):1068–1071. Scholar
  14. Hayward P, Parent J (2009) Modeling the influence of the modifiable areal unit problem (MAUP) on poverty in Pennsylvania. Pa Geogr 47(1):120–135Google Scholar
  15. Hoover EM, Vernon R (1959) Anatomy of a metropolis; the changing distribution of people and jobs within the New York metropolitan region, New York metropolitan region study. Harvard University Press, Cambridge, MA (New York metropolitan region. Study: no. 1).
  16. Huang B (2017) Comprehensive geographic information systems. ElsevierGoogle Scholar
  17. Janssen HJ, Van Ham M (2019) Resituating the local in cohesion and territorial development report on multi-scalar patterns of inequalities.
  18. Kyriakidis PC, Journel AG (1999) Geostatistical space-time models: a review. Math Geol 31(6):651–684. Scholar
  19. Lesnard L (2009) Setting cost in optimal matching to uncover contemporaneous socio-temporal patterns. Sociol Methods Res. Scholar
  20. Lloyd CD et al (2017) Exploring the utility of grids for analysing long term population change. Comput Environ Urban Syst 66:1–12. Scholar
  21. Miller HJ (2015) Space-time data science for a speedy world. I/S J Law Policy Inf Soc 10(3):705–720. Scholar
  22. Openshaw S (1983) The modifiable area unit problem. Concepts Tech Mod Geogr 38:1–41. Scholar
  23. Prouse V et al (2014) How and when scale matters: the modifiable areal unit problem and income inequality in Halifax. Can J Urban Res 23(1):61–82Google Scholar
  24. Rowe F (2017) The CHilean Internal Migration (CHIM) database: temporally consistent spatial data for the analysis of human mobility. Region 4(3):1. Scholar
  25. Rowe F, Casado-Díaz JM, Martínez-Bernabéu L (2017a) Functional labour market areas for Chile. Region 4(3):7. Scholar
  26. Rowe F, Corcoran J, Bell M (2017b) The returns to migration and human capital accumulation pathways: non-metropolitan youth in the school-to-work transition. Ann Reg Sci 59(3):819–845 (Springer, Berlin, Heidelberg). Scholar
  27. Sanger F, Nicklen S (1977) DNA sequencing with chain-terminating 74(12):5463–5467Google Scholar
  28. Studer M (2013) WeightedCluster library manual, pp 1–34.
  29. Teernstra AB, Van Gent WPC (2012) Puzzling patterns in neighborhood change: upgrading and downgrading in highly regulated urban housing markets. Urban Geogr 33(1):91–119. Scholar
  30. Warren Liao T (2005) Clustering of time series data—a survey. Pattern Recogn 38(11):1857–1874. Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Geographic Data Science Lab, Department of Geography and PlanningUniversity of LiverpoolLiverpoolUK
  2. 2.Ordnance Survey LimitedSouthamptonUK

Personalised recommendations