Skip to main content

Contribution Towards Smart Cities: Exploring Block Level Census Data for the Characterization of Change in Lisbon

  • Chapter
  • First Online:
Trends in Spatial Analysis and Modelling

Abstract

The interest in using information to improve the quality of living in large urban areas and the efficiency of its governance has been around for decades. Nevertheless, recent developments in information and communications technology have sparked new ideas in academic research, all of which are usually grouped under the umbrella term of Smart Cities. The concept of Smart City can be defined as cities that are lived, managed and developed in an information-saturated environment. However, there are still several significant challenges that need to be tackled before we can realize this vision. In this study we aim at providing a small contribution in this direction, by maximizing the usefulness of the already available information resources. One of the most detailed and geographically relevant information resources available for studying cities is the census, more specifically, the data available at block level. In this study we use self-organizing maps (SOM) to explore the block level data included in the 2001 and 2011 Portuguese censuses for the city of Lisbon. We focus on measuring change, proposing new ways to compare the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at providing a twofold portrait: showing how Lisbon evolved during the first decade of the twenty-first century and how both the census dataset and the SOMs can be used to produce an informational framework for micro analysis of urban contexts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Andrienko NV, Andrienko G (2006) In: Springer (ed) Exploratory analysis of spatial and temporal data: a systematic approach. Science & Business Media, London

    Google Scholar 

  • Andrienko G et al (2008) Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research. Inf Vis 7(3–4):173–180

    Article  Google Scholar 

  • Andrienko G et al (2010) Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. Comput Graph Forum 29(3):913–922

    Article  Google Scholar 

  • Bação F, Lobo V Painho M (2004) Geo-self-organizing map (Geo-SOM) for building and exploring homogeneous regions. Geogr Inf Sci

    Google Scholar 

  • Bação F, Lobo V, Painho M (2005a) Applying genetic algorithms to zone design. Soft Comput 9(5):341–348

    Article  Google Scholar 

  • Bação F, Lobo V, Painho M (2005b) Self-organizing maps as substitutes for k-means clustering. Comput Sci–ICCS 2005(3516):476–483

    Google Scholar 

  • Bação F, Lobo V, Painho M (2005c) The self-organizing map, the Geo-SOM, and relevant variants for geosciences. Comput Geosci 31(2):155–163

    Article  Google Scholar 

  • Birch EL, Wachter SM (2011) Global Urbanization. J Reg Sci 51(5):1026–1028

    Article  Google Scholar 

  • Bloom LM, Pedler PJ, Wragg GE (1996) Implementation of enhanced areal interpolation using MapInfo. Comput Geosci 22(5):459–466

    Article  Google Scholar 

  • Braulio-Gonzalo M, Bovea MD, Ruá MJ (2015) Sustainability on the urban scale: proposal of a structure of indicators for the Spanish context. Environ Impact Assess Rev 53:16–30

    Article  Google Scholar 

  • Chourabi H et al (2012) Understanding smart cities: an integrative framework. In: 2012 45th Hawaii international conference on system sciences, pp 2289–2297

    Google Scholar 

  • Donoho D (2000) High-dimensional data analysis: the curses and blessings of dimensionality. In: AMS math challenges lecture, pp 1–33

    Google Scholar 

  • Eicher CL, Brewer CA (2001) Dasymetric mapping and areal interpolation: implementation and evaluation. Cartogr Geogr Inf Sci 28(2):125–138

    Article  Google Scholar 

  • Fiedler R, Schuurman N, Hyndman J (2006) Improving census-based socioeconomic GIS for public policy: recent immigrants, spatially concentrated poverty and housing need in Vancouver. ACME 4(1):145–169

    Google Scholar 

  • Fink EL (2009) The FAQs on data transformation. Commun Monogr 76(January 2015):379–397

    Article  Google Scholar 

  • Fisher PF, Langford M (1996) Modeling sensitivity to accuracy in classified imagery: a study of areal interpolation by dasymetric mapping. Prof Geogr 48(3):299–309

    Article  Google Scholar 

  • Fukunaga K (1990) Statistical pattern stas-tical pattern recognition. Pattern Recogn 22(7):833–834

    Google Scholar 

  • Gorricha J, Lobo V (2012) Improvements on the visualization of clusters in geo-referenced data using Self-Organizing Maps. Comput Geosci 43:177–186

    Article  Google Scholar 

  • Guha S, Rastogi R, Shim K (1998) Cure. ACM SIGMOD Rec 27(2):73–84

    Article  Google Scholar 

  • Hajek P, Henriques R, Hajkova V (2014) Visualising components of regional innovation systems using self-organizing maps-Evidence from European regions. Technol Forecast Soc Chang 84:197–214

    Article  Google Scholar 

  • Henriques R, Bacao F, Lobo V (2012) Exploratory geospatial data analysis using the GeoSOM suite. Comput Environ Urban Syst 36(3):218–232

    Article  Google Scholar 

  • Hernández-Muñoz JM et al (2011) Smart cities at the forefront of the future internet. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6656, pp 447–462

    Google Scholar 

  • Howard G, Lubbe S, Klopper R (2011) The impact of information quality on information research. Manag Inf Res Des 288

    Google Scholar 

  • Huang L, Yan L, Wu J (2016) Assessing urban sustainability of Chinese megacities: 35 years after the economic reform and open-door policy. Landsc Urban Plan 145:57–70

    Article  Google Scholar 

  • Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  • Jelinski DE, Wu J (1996) The modifiable areal unit problem and implications for landscape ecology. Landsc Ecol 11(3):129–140

    Article  Google Scholar 

  • Kohonen T (1995) Self organizing maps. Springer series in information sciences, 30. Springer, Berlin, p 521

    Google Scholar 

  • Kohonen T (2013) Essentials of the self-organizing map. Neural Netw 37:52–65. Available at: http://dx.doi.org/10.1016/j.neunet.2012.09.018

    Article  Google Scholar 

  • Koua EL (2003) Using self-organizing maps for information visualization and knowledge discovery in complex geospatial datasets. In: Proceedings of 21st international cartographic renaissance (ICC), pp 1694–1702

    Google Scholar 

  • Koua EL, Kraak M (2004) Alternative visualization of large geospatial datasets. Cartogr J 41(3):217–228

    Article  Google Scholar 

  • Lee ACD, Rinner C (2014) Visualizing urban social change with self-organizing maps: Toronto neighbourhoods, 1996–2006. Habitat Int 45:92–98

    Article  Google Scholar 

  • Lombardi P et al (2012) Modelling the smart city performance. Innov: Eur J Soc Sci Res 25(2):137–149

    Google Scholar 

  • Longo G, Gerometta J, Haussermann H (2005) Social innovation and civil society in urban governance: strategies for an inclusive city. Urban Stud 42(11):2007–2021

    Article  Google Scholar 

  • Manley D (2014) Scale, aggregation, and the modifiable areal unit problem. In: Handbook of regional science, pp 1157–1171

    Google Scholar 

  • Mennis J, Guo D (2009) Spatial data mining and geographic knowledge discovery—an introduction. Comput Environ Urban Syst 33(6):403–408

    Article  Google Scholar 

  • Nam T, Pardo TA (2011) Conceptualizing smart city with dimensions of technology, people, and institutions. In: Proceedings of the 12th annual international digital government research conference on digital government innovation in challenging times – dg.o ‘11, p 282

    Google Scholar 

  • Nelson JK, Brewer CA (2015) Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem. Cartogr Geogr Inf Sci 0406(October):1–16

    Google Scholar 

  • Openshaw S (1984) Ecological fallacies and the analysis of areal census data. Environ Plan A 16(1):17–31

    Article  Google Scholar 

  • Penn BS (2005) Using self-organizing maps to visualize high-dimensional data. Comput Geosci 31(5):531–544

    Article  Google Scholar 

  • Polèse M (2010) The resilient city: on the determinants of successful urban economies. INRS, Montreal, p 32

    Google Scholar 

  • Roche S (2014) Geographic information science I: why does a smart city need to be spatially enabled? Prog Hum Geogr 38(5):0309132513517365

    Article  Google Scholar 

  • Root ED (2012) Moving neighborhoods and health research forward: using geographic methods to examine the role of spatial scale in neighborhood effects on health. Ann Assoc Am Geogr 102(5):986–995

    Article  Google Scholar 

  • Silva CN, Syrett S (2006) Governing Lisbon: evolving forms of city governance. Int J Urban Reg Res 30(March):98–119

    Article  Google Scholar 

  • Skupin A (2002) A cartographic approach to visualizing conference abstracts. Ieee Comput Graph Appl 22(February):50–58

    Article  Google Scholar 

  • Skupin A, Agarwal P (2008) Introduction: what is a self-organizing map? Self-organising maps: applications in geographic information science. Wiley, Chichester

    Google Scholar 

  • Skupin A, Hagelman R (2005) Visualizing demographic trajectories with self-organizing maps. GeoInformatica 9(2):159–179

    Article  Google Scholar 

  • Steinbach M, Karypis G, Kumar V (2000) A comparison of document clustering techniques. In: KDD workshop on text mining, pp 1–2. Available at: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4721382

  • Ultsch A, Siemon HP (1990) Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of the International Neural Network Conference (INNC-90), pp 305–308

    Google Scholar 

  • United Nations (2013) World economic and social survey 2013. Available at: http://esa.un.org/wpp/documentation/pdf/WPP2012_ KEY FINDINGS.pdf

  • United Nations Economic Commission for Europe (2006). Conference of European statisticians recommendations for the 2010 censuses of. In New York and Geneva

    Google Scholar 

  • van der Maaten L, Postma E, van den Herik J (2009) Dimensionality reduction: a comparative review. J Mach Learn Res 10(February):1–41

    Google Scholar 

  • Veiga L (2014) Economic crisis and the image of Portugal as a tourist destination: the hospitality perspective. Worldw Hosp Tour Themes 6(5):475–479

    Article  Google Scholar 

  • Vesanto J (1999) SOM−based data visualization methods. Intell Data Anal 3(2):111–126

    Article  Google Scholar 

  • Waddell P (2007) UrbanSim: modeling urban development for land use, transportation, and environmental planning. J Am Plan Assoc 68(3):297–314

    Article  Google Scholar 

  • Wier M et al (2009) An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. Accid Anal Prev 41(1):137–145

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fernando José Ferreira Lucas Bação .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Bação, F.J.F.L., Henriques, R., Antunes, J. (2018). Contribution Towards Smart Cities: Exploring Block Level Census Data for the Characterization of Change in Lisbon. In: Behnisch, M., Meinel, G. (eds) Trends in Spatial Analysis and Modelling. Geotechnologies and the Environment, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-52522-8_4

Download citation

Publish with us

Policies and ethics