IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v18y2016i1d10.1007_s10109-015-0222-6.html
   My bibliography  Save this article

Testing block subdivision algorithms on block designs

Author

Listed:
  • Natalie Wiseman

    (Concordia University)

  • Zachary Patterson

    (Concordia University)

Abstract

Integrated land use–transportation models predict future transportation demand taking into account how households and firms arrange themselves partly as a function of the transportation system. Recent integrated models require parcels as inputs and produce household and employment predictions at the parcel scale. Block subdivision algorithms automatically generate parcel patterns within blocks. Evaluating block subdivision algorithms is done by way of generating parcels and comparing them to those in a parcel database. Three block subdivision algorithms are evaluated on how closely they reproduce parcels of different block types found in a parcel database from Montreal, Canada. While the authors who developed each of the algorithms have evaluated them, they have used their own metrics and block types to evaluate their own algorithms. This makes it difficult to compare their strengths and weaknesses. The contribution of this paper is in resolving this difficulty with the aim of finding a better algorithm suited to subdividing each block type. The proposed hypothesis is that given the different approaches that block subdivision algorithms take, it’s likely that different algorithms are better adapted to subdividing different block types. To test this, a standardized block type classification is used that consists of mutually exclusive and comprehensive categories. A statistical method is used for finding a better algorithm and the probability it will perform well for a given block type. Results suggest the oriented bounding box algorithm performs better for warped non-uniform sites, as well as gridiron and fragmented uniform sites. It also produces more similar parcel areas and widths. The Generalized Parcel Divider 1 algorithm performs better for gridiron non-uniform sites. The Straight Skeleton algorithm performs better for loop and lollipop networks as well as fragmented non-uniform and warped uniform sites. It also produces more similar parcel shapes and patterns.

Suggested Citation

  • Natalie Wiseman & Zachary Patterson, 2016. "Testing block subdivision algorithms on block designs," Journal of Geographical Systems, Springer, vol. 18(1), pages 17-43, January.
    • Handle: RePEc:kap:jgeosy:v:18:y:2016:i:1:d:10.1007_s10109-015-0222-6
    DOI: 10.1007/s10109-015-0222-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10109-015-0222-6
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10109-015-0222-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Waddell, Paul & Ulfarsson, Gudmundur F. & Franklin, Joel P. & Lobb, John, 2007. "Incorporating land use in metropolitan transportation planning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(5), pages 382-410, June.
    2. B.A. Sandalack & F.G. Alaniz Uribe & A. Eshghzadeh Zanjani & A. Shiell & G.R. McCormack & P.K. Doyle-Baker, 2013. "Neighbourhood type and walkshed size," Journal of Urbanism: International Research on Placemaking and Urban Sustainability, Taylor & Francis Journals, vol. 6(3), pages 236-255, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hiroyuki Usui, 2019. "Statistical distribution of building lot depth: Theoretical and empirical investigation of downtown districts in Tokyo," Environment and Planning B, , vol. 46(8), pages 1499-1516, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eliasson, Jonas & Savemark, Christian & Franklin, Joel, 2020. "The impact of land use effects in infrastructure appraisal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 262-276.
    2. Stevens, Nicholas & Baker, Douglas & Freestone, Robert, 2010. "Airports in their urban settings: towards a conceptual model of interfaces in the Australian context," Journal of Transport Geography, Elsevier, vol. 18(2), pages 276-284.
    3. Brian Lee & Paul Waddell, 2010. "Residential mobility and location choice: a nested logit model with sampling of alternatives," Transportation, Springer, vol. 37(4), pages 587-601, July.
    4. Niu, Fangqu & Li, Jun, 2018. "Modeling the population and industry distribution impacts of urban land use policies in Beijing," Land Use Policy, Elsevier, vol. 70(C), pages 347-359.
    5. Bernardo A. Furtado & Miguel A. Fuentes & Claudio J. Tessone, 2019. "Policy Modeling and Applications: State-of-the-Art and Perspectives," Complexity, Hindawi, vol. 2019, pages 1-11, February.
    6. Waddell, Paul & Boeing, Geoff & Gardner, Max & Porter, Emily, 2018. "An Integrated Pipeline Architecture for Modeling Urban Land Use, Travel Demand, and Traffic Assignment," SocArXiv 74zaw, Center for Open Science.
    7. Su, Hailong & Wu, Jia Hao & Tan, Yinghui & Bao, Yuanqiu & Song, Bing & He, Xinghua, 2014. "A land use and transportation integration method for land use allocation and transportation strategies in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 329-353.
    8. Zhenqi Zhou & Zhen Xu, 2020. "Detecting the Pedestrian Shed and Walking Route Environment of Urban Parks with Open-Source Data: A Case Study in Nanjing, China," IJERPH, MDPI, vol. 17(13), pages 1-16, July.
    9. Mörtberg, Ulla & Goldenberg, Romain & Kalantari, Zahra & Kordas, Olga & Deal, Brian & Balfors, Berit & Cvetkovic, Vladimir, 2017. "Integrating ecosystem services in the assessment of urban energy trajectories – A study of the Stockholm Region," Energy Policy, Elsevier, vol. 100(C), pages 338-349.
    10. Wood, Liza & Scott, Tyler A., 2022. "Transportation agencies as consumers and producers of science: The case of state, regional, and county transportation agencies in California," Transport Policy, Elsevier, vol. 128(C), pages 153-165.
    11. Canca, David & Zarzo, Alejandro & Algaba, Encarnación & Barrena, Eva, 2013. "Macroscopic attraction-based simulation of pedestrian mobility: A dynamic individual route-choice approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 428-442.
    12. Soria-Lara, Julio A. & Aguilera-Benavente, Francisco & Arranz-López, Aldo, 2016. "Integrating land use and transport practice through spatial metrics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 330-345.
    13. Nimrod Serok & Orr Levy & Shlomo Havlin & Efrat Blumenfeld-Lieberthal, 2019. "Unveiling the inter-relations between the urban streets network and its dynamic traffic flows: Planning implication," Environment and Planning B, , vol. 46(7), pages 1362-1376, September.
    14. Sakai, Takanori & Kawamura, Kazuya & Hyodo, Tetsuro, 2019. "Evaluation of the spatial pattern of logistics facilities using urban logistics land-use and traffic simulator," Journal of Transport Geography, Elsevier, vol. 74(C), pages 145-160.
    15. Zahra Kalantari & Sara Khoshkar & Helena Falk & Vladimir Cvetkovic & Ulla Mörtberg, 2017. "Accessibility of Water-Related Cultural Ecosystem Services through Public Transport—A Model for Planning Support in the Stockholm Region," Sustainability, MDPI, vol. 9(3), pages 1-16, February.
    16. Berjisian, Elmira & Habibian, Meeghat, 2019. "Developing a pedestrian destination choice model using the stratified importance sampling method," Journal of Transport Geography, Elsevier, vol. 77(C), pages 39-47.
    17. Marko Kryvobokov & Aurélie Mercier & Alain Bonnafous & Dominique Bouf, 2013. "Simulating housing prices with UrbanSim: predictive capacity and sensitivity analysis," Letters in Spatial and Resource Sciences, Springer, vol. 6(1), pages 31-44, March.
    18. Sevcíková, Hana & Raftery, Adrian E. & Waddell, Paul A., 2011. "Uncertain benefits: Application of Bayesian melding to the Alaskan Way Viaduct in Seattle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 540-553, July.
    19. Poulicos Prastacos & Nektarios Chrysoulakis, 2011. "Urban Atlas, land use modelling and spatial metric techniques," ERSA conference papers ersa11p1406, European Regional Science Association.
    20. Ying Jin, 2011. "What are the key effects of road pricing upon an integral city region? The case of the London conurbation," ERSA conference papers ersa10p1482, European Regional Science Association.

    More about this item

    Keywords

    Automatic block subdivision; Algorithm evaluation; Urban and regional planning; Integrated modelling;
    All these keywords.

    JEL classification:

    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R49 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Other
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R52 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Land Use and Other Regulations

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jgeosy:v:18:y:2016:i:1:d:10.1007_s10109-015-0222-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.