Skip to main content

Computational Approaches for Urban Environments: An Editorial

  • Chapter
  • First Online:
Computational Approaches for Urban Environments

Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 13))

Abstract

Cities are under continuous pressure due to an increasing urbanization which will have far-reaching consequences for housing, transportation, retail, etc. To cope with these challenges, methodological advances in quantitative modeling coupled with growing amounts of spatial and spatiotemporal data can add significantly to our understanding of how cities function. Because the added value of data-driven approaches to analyze urban environments is promising but still in its infancy, the present volume aims to promote the application of advanced computational methodologies to achieve a better understanding of our cities and the underlying mechanisms.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

  • Anas A, Arnott R, Small K (1998) Urban spatial structure. J Econ Lit 36:1426–1464

    Google Scholar 

  • Arribas-Bel D, Nijkamp P, Scholten H (2011) Multidimensional urban sprawl in Europe: A self-organizing map approach. Comput Environ Urban Syst 35:263–275

    Article  Google Scholar 

  • Batty M (2008) The size, scale, and shape of cities. Science 319:769–771

    Article  Google Scholar 

  • Batty M (2013) New science of cities. MIT Press, Cambridge

    Google Scholar 

  • Batty M, Longley P (1994) Fractal cities: a geometry of form and function. Academic Press, London

    Google Scholar 

  • Bettencourt L (2013) The origins of scaling in cities. Science 340:1438–1441

    Article  Google Scholar 

  • Birkin M (2009) Geocomputation. In: Kitchin R, Thrift N (eds) International encyclopedia of human geography. Elsevier, Oxford, pp 376–381

    Chapter  Google Scholar 

  • Brunauer W, Lang S, Umlauf N (2013) Modelling house prices using multilevel structured additive regression. Stat Model 13:95–123

    Article  Google Scholar 

  • Calabresea F, Diaob M, Lorenzo D, Ferreira J, Ratti C (2013) Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Trans Res Part C: Emerg Technol 26:301–313

    Article  Google Scholar 

  • Clarke K (2014) Why simulate cities? GeoJournal 79:129–136

    Article  Google Scholar 

  • Couclelis H (1998) Geocomputation in context. In: Longley P, Brooks S, McDonnell R, MacMillan B (eds) Geocomputation: a primer. Wiley, Chichester, pp 17–29

    Google Scholar 

  • De Vos J, Witlox F (2013) Transportation policy as spatial planning tool; reducing urban sprawl by increasing travel costs and clustering infrastructure and public transportation. J Transp Geogr 33:117–125

    Article  Google Scholar 

  • Fischer M (2006) Spatial analysis and geocomputation. Springer, Berlin

    Google Scholar 

  • Fujita M, Thisse J-F (2013) Economics of agglomeration: cities, industrial location, and globalization. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Glaeser E (2011) Cities, productivity, and quality of Life. Science 333:592–594

    Article  Google Scholar 

  • Goodchild M (2010) Twenty years of progress: GIScience in 2010. J Spat Inf Sci 1:3–20

    Google Scholar 

  • Grekousis G, Manetos P, Photis YN (2013) Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities 30:193–203

    Article  Google Scholar 

  • Gu J, Zhu M, Jiang L (2011) Housing price forecasting based on genetic algorithm and support vector machine. Expert Syst Appl 38:3383–3386

    Article  Google Scholar 

  • Hagenauer J, Helbich M (2013a) Hierarchical self-organizing maps for clustering spatiotemporal data. Int J Geogr Inf Sci 27:2026–2042

    Article  Google Scholar 

  • Hagenauer J, Helbich M (2013b) Contextual neural gas for spatial clustering and analysis. Int J Geogr Inf Sci 27:251–266

    Article  Google Scholar 

  • Hagenauer J, Helbich M, Leitner M (2011) Visualization of crime trajectories with self-organizing maps: a case study on evaluating the impact of hurricanes on spatio-temporal crime hotspots. In: 25th international cartographic conference, Paris, France

    Google Scholar 

  • Hall P (1993) Forces shaping urban Europe. Urban Stud 30:883–898

    Article  Google Scholar 

  • Helbich M, Leitner M (2012) Evaluation of spatial cluster detection algorithms for crime locations. In: Gaul W, Geyer-Schulz A, Schmidt-Thieme L, Kunze J (ed) Challenges at the interface of data analysis, computer science, and optimization. Studies in classification, data analysis, and knowledge organization. Springer, Berlin, pp 193–201

    Google Scholar 

  • Helbich M, Jochem A, Mücke W, Höfle B (2013a) Boosting the predictive accuracy of urban hedonic house price models through airborne laser scanning. Comput Environ Urban Syst 39:81–92

    Article  Google Scholar 

  • Helbich M, Hagenauer J, Leitner M, Edwards R (2013b) Exploration of unstructured narrative crime reports: an unsupervised neural network and point pattern analysis approach. Cartogr Geogr Inf Sci 40:326–336

    Article  Google Scholar 

  • Helbich M, Brunauer W, Hagenauer J, Leitner M (2013c) Data-driven regionalization of housing markets. Ann Assoc Am Geogr 103:871–889

    Article  Google Scholar 

  • Helbich M, Brunauer W, Vaz E, Nijkamp P (2014) Spatial heterogeneity in hedonic house price models: the case of Austria. Urban Stud 51:390–411

    Article  Google Scholar 

  • Jokar Arsanjani J, Helbich M, Bakillah M, Hagenauer J, Zipf A (2013a) Towards mapping land use patterns from volunteered geographic information. Int J Geogr Inf Sci 27:2264–2278

    Article  Google Scholar 

  • Jokar Arsanjani J, Helbich M, Vaz E (2013b) Spatiotemporal simulation of urban growth patterns using agent-based modeling: the case of Tehran. Cities 32:33–42

    Article  Google Scholar 

  • Jokar Arsanjani J, Helbich M, Mousivand A (2014) A morphological approach to predicting urban expansion. Trans GIS 18(2):219–233 (online first). doi: 10.111 1/tgis.12031

    Google Scholar 

  • Kwan M-P, Schwanen T (2009) Quantitative revolution 2: the critical (re)turn. Prof Geogr 61:283–291

    Article  Google Scholar 

  • Law J, Quick M (2013) Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach. J Geogr Syst 15:89–113

    Article  Google Scholar 

  • Lazer D et al (2009) Computational social science. Sciences 323:721–723

    Article  Google Scholar 

  • Leitner M, Helbich M (2011) The impact of hurricanes on crime: a spatio-temporal analysis in the city of Houston, TX. Cartogr Geogr Inf Sci 38:214–222

    Article  Google Scholar 

  • Malleson N, Heppenstall A, See L (2013) Using an agent-based crime simulation to predict the effects of urban regeneration on individual household burglary risk. Environ Plan B 40:405–426

    Article  Google Scholar 

  • Miller H, Goodchild M (2014) Data-driven geography. GeoJournal (online first)

    Google Scholar 

  • Mimis A, Rovolis A, Stamou M (2013) Property valuation with artificial neural network: the case of Athens. J Prop Res 30:128–143

    Article  Google Scholar 

  • Nakaya T, Yano K (2010) Visualising crime clusters in a space-time cube: an exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Trans GIS 14:223–239

    Article  Google Scholar 

  • Openshaw S, Abrahart R (2000) GeoComputation. Taylor and Francis, London

    Google Scholar 

  • Pacione M (2009) Urban geography: a global perspective. Routledge, New York

    Google Scholar 

  • Pijanowski B, Tayyebi A, Doucette J, Pekin BK (2014) A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance (HPC) environment. Environ Model Softw 51:250–268

    Article  Google Scholar 

  • Shafizadeh-Moghadam H, Helbich M (2015) Spatiotemporal variability of urban growth factors: a global and local perspective on the megacity of Mumbai. Int J Appl Earth Obs Geoinf 35(Part B):187–198

    Google Scholar 

  • Solecki W, Seto K, Marcotullio P (2013) It’s time for an urbanization science. Environment 55:12–16

    Article  Google Scholar 

  • Torrens P (2012) Moving agent-pedestrians through space and time. Ann Assoc Am Geogr 102:35–66

    Article  Google Scholar 

  • United Nations (2014) Department of economic and social affairs, population division, http://www.un.org/en/development/desa/population/

  • Vaz E, Caetano M, Nijkamp P, Painho M (2012) A multi-scenario prospection of urban change – a study on urban growth in the Algarve. Landsc Urban Plan 104:201–211

    Article  Google Scholar 

  • Wang F, Guo D, McLafferty S (2012) Constructing geographic areas for cancer data analysis: a case study on late-stage breast cancer risk in Illinois. Appl Geogr 35:1–11

    Article  Google Scholar 

  • Xu S, Vosselman G, Oude Elberink S (2014) Multiple-entity based classification of airborne laser scanning data in urban areas. ISPRS J Photogramm Remote Sens 88:1–15

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marco Helbich .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Helbich, M., Jokar Arsanjani, J., Leitner, M. (2015). Computational Approaches for Urban Environments: An Editorial. In: Helbich, M., Jokar Arsanjani, J., Leitner, M. (eds) Computational Approaches for Urban Environments. Geotechnologies and the Environment, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-11469-9_1

Download citation

Publish with us

Policies and ethics