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Variography and Morphometry for Classifying Building Centroids: Protocol, Data and Script

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Book cover Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

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Abstract

Different spatial patterns of urban growth exist such as infill, edge-expansion and leapfrog development. This paper presents a methodology, and a corresponding script, that classify new residential buildings as patterns of urban growth. The script performs a combination of variography and morphometry over building centroids on two different dates. The test data is made of the building centroids of 2002 and 2017 for Centre-Var, a region located in southern France. The different bounding regions, yield from series of morphological closings, allow classifying the building centroids that appeared between 2002 and 2017 into different categories of spatial patterns of urban growth. The final classification is made according to the degree of clustering/scattering of new centroids and to their locations regarding existing urban areas. Preliminary results show that this protocol is able to provide useful insights regarding the degree of contribution of each new residential building to the following patterns of urban growth: clustered infill, scattered infill, clustered edge-expansion, scattered edge-expansion, clustered leapfrog and scattered leapfrog. Open access to the script and to the test region data is provided.

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Notes

  1. 1.

    French national institute for statistical and economic studies.

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Author information

Authors and Affiliations

  1. Université Côte-Azur, CNRS, ESPACE, Nice, France

    Joan Perez & Alexandre Ornon

  2. Department of Urban Engineering, Housing and Urban Analysis Laboratory, The University of Tokyo, Tokyo, Japan

    Hiroyuki Usui

Authors
  1. Joan Perez
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  2. Alexandre Ornon
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  3. Hiroyuki Usui
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Corresponding author

Correspondence to Joan Perez .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

Appendix: Data and Script

Appendix: Data and Script

Files

- CV_PT_Ext_0217.gpkg (9.8 MB)

- PT_Classification (v.1.2 - 13.6 kB)

- CV_PT_Ext_0217_RES.gpkg (48.9 MB)

- README.txt (2.9 kB)

Title

Variography and Morphometry for Classifying Building Centroids as Patterns of Urban Growth v1.2

Link

https://zenodo.org/record/3899981

Description

This upload contains a dataset ready to be processed, an R script (v1.2) and a dataset with the associated results of the processing steps. The dataset is a GeoPackage file (EPSG:2154 - RGF93/Lambert-93 - Projected) named “CV_PT_Ext_0217.gpkg” containing two layers (1) “CV_Building_PT”: a point feature class made of the centroids of residential buildings in 2002 and 2017 extracted from the French BD TOPO® (National Geographic Institute). This layer possesses two attribute columns indicative of the building presence for each period: “Pres_2002” and “Pres_2017” (modalities: “1” for building presence; “0” otherwise). (2) “Centre_Var_ext”: a polygon feature class related to the extent of the case study: a region in southern France named Centre-Var. The script performs a combination of variography analysis and morphological closings over the building centroids. The different bounding regions then allow classifying new residential buildings (the ones that appeared between 2002 and 2017) into different categories of patterns of urban growth according to their degrees of clustering/scattering and to their locations regarding existing urban areas. The GeoPackage associated with the results “CV_PT_Ext_0217_RES.gpkg” contains two additional layers (3) “PT_CLASS” a point feature class made of the new building centroids with an attribute column “cat” related to the classification outputs. The categories are as follows: 1 - clustered infill, 2 - scattered infill, 3 - clustered edge-expansion, 4 - scattered edge-expansion, 5 - clustered leapfrog and 6 - scattered leapfrog. (4) “BR_Clipping”: a polygon feature class made of the different bounding regions clipped together.

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Perez, J., Ornon, A., Usui, H. (2020). Variography and Morphometry for Classifying Building Centroids: Protocol, Data and Script. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-58811-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58810-6

  • Online ISBN: 978-3-030-58811-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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