Advertisement

The detection of natural cities in the Netherlands—Nocturnal satellite imagery and Zipf’s law

  • Rolf Bergs
Original Paper
  • 31 Downloads

Abstract

How to detect the true extent of cities in highly urbanized countries? This paper addresses the delineation of natural urban and non-urban space and its change based on a wider understanding of spatial heterogeneity. The Netherlands is selected as a case study. “Natural” means the extent of urban space irrespective of administrative boundaries. The database, used for this study, is radiance-calibrated nocturnal satellite imagery from the Defence Meteorological Satellite Program (DMSP). Extraction of cities is done by K-means segmentation. Based on the variance of luminosity it is possible to detect natural urban space. After removal of outliers in the skewed pixel distributions and after correction of “blooming” (over-glow of light emission) Zipf’s law is then applied as a test for segmentation adequacy. The comparative analysis for the years 1996 and 2011 shows that the rank-size distribution of natural cities is well confirmed by Zipf’s law, in contrast to that of administrative cities.

Keywords

Natural cities Segmentation of space Satellite imagery Zipf’s law 

Die Abgrenzung natürlicher Städte in den Niederlanden: Nachtsatellitenbilder und das Zipf-Gesetz

Zusammenfassung

Wie lässt sich die wahre Ausdehnung von Städten in hochgradig urbanisierten Staaten erkennen? Die Studie behandelt die Differenzierung von natürlichem städtischen und nicht-städtischen Raum und seinem Wandel in einem erweiterten Sinne von räumlicher Heterogenität. Die Niederlande werden als Fallstudie betrachtet. „Natürlich“ meint hierbei die Unabhängigkeit städtischer Ausdehnung von administrativ gezogenen Grenzen. Die verwendete Datenbasis sind radianzkalibrierte Nachtsatellitenbilder des Defence Meteorological Satellite Program (DMSP). Die Extrahierung der Städte erfolgt unter Anwendung der K-means-Segmentierung. Auf Basis der Varianz der Lichtemission lässt sich so der natürliche städtische Raum sichtbar machen. Nach Beseitigung von statistischen Ausreißern in den schiefen Pixel-Verteilungen und nach Korrektur der Verzerrungen durch Überstrahlungseffekte wird die gewonnene Segmentierung mit Hilfe des Zipf-Gesetzes auf Angemessenheit getestet. Die vergleichende Analyse für die Jahre 1996 und 2011 zeigt, dass die Rangverteilung der natürlichen Städte durch das Zipf-Gesetz bestätigt wird, ganz im Gegensatz zur Rangverteilung der Städte in ihren Verwaltungsgrenzen.

JEL-Classification

O 18 R 12 

Notes

Acknowledgements

The author thanks Rüdiger Budde and two anonymous referees for help and useful comments. An earlier draft version of this paper titled “Segmentation of urban and non-urban space in the Netherlands—Testing Zipf’s law with nocturnal satellite imagery” had been prepared for the ERSA 2017 conference in Groningen. It is accessible on: https://www.researchgate.net/publication/320234838_Segmentation_of_Urban_and_Non-Urban_Space_in_the_Netherlands_-_Testing_Zipf%27s_Law_with_Nocturnal_Satellite_Imagery

References

  1. Alexander C (1965) The city is not a tree. Arch Forum 122(1):58–62Google Scholar
  2. Amaral S, Monteiro AVM, Camara G, Quintanilha JA (2006) DMSP/OLS night-time light imagery for urban population estimates in the Brazilian Amazon. Int J Remote Sens 27(5):855–870CrossRefGoogle Scholar
  3. Bagan H, Yamagata Y (2015) Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data. Gisci Remote Sens 52(6):765–780CrossRefGoogle Scholar
  4. Bergs R (2012) Cross-border Cooperation, Regional Disparities and Integration of Markets in the EU. J Borderl Stud 27(3):345–363CrossRefGoogle Scholar
  5. Brakman S, Garretsen H, van Marrewijk C, van den Berg M (1999) The return of Zipf: understanding the rank-size distribution. J Reg Sci 39(1):183–213CrossRefGoogle Scholar
  6. Brakman S, Garretsen H, van Marrewijk C (2009) The new introduction to geographical economics. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  7. Budde R, Neumann U (2016) Gravitation and dispersion—a disaggregate view on urban agglomeration and sprawl in Germany. Paper, Scorus Conference, LisbonGoogle Scholar
  8. Duranton G (2007) Urban evolutions: the fast, the slow, and the still. Am Econ Rev 97(1):197–221CrossRefGoogle Scholar
  9. Eeckhout J (2004) Gibrat’s law for (all) cities. Am Econ Rev 94(5):1429–1451CrossRefGoogle Scholar
  10. Egger PH, Lumeau G, Püschel N (2017) Natural city growth in the people’s republic of China. Asian Dev Rev 34(2):51–85CrossRefGoogle Scholar
  11. Elvidge CD, Safran J, Nelson IL, Tuttle BT, Hobson VR, Baugh KE, Dietz JB, Erwin EH (2004) Area and position accuracy of DMSP nighttime lights data. In: Lunetta RS, Lyon JG (eds) Remote sensing and GIS accuracy assessment. CRC Press, Boca Raton, pp 281–291CrossRefGoogle Scholar
  12. Fujita M, Krugman P, Venables AJ (1999) The spatial economy: cities regions and international trade. MIT Press, Cambridge (Mass.)Google Scholar
  13. Gabaix X (1999a) Zipf’s law and the growth of cities. Am Econ Rev 89(2):129–132CrossRefGoogle Scholar
  14. Gabaix X (1999b) Zipf law for cities: an explanation. Q J Econ 114(3):739–767CrossRefGoogle Scholar
  15. Gabaix X (2016) Power laws in economics: an introduction. J Econ Perspect 30(1):185–206CrossRefGoogle Scholar
  16. Gabaix X, Ibragimov R (2011) Rank-1/2: A simple way to improve the OLS estimation of tail exponents. J Bus Econ Stat 29(1):24–39CrossRefGoogle Scholar
  17. Gan L, Li D, Song S (2006) Is the Zipf law spurious in explaining city-size distributions? Econ Lett 92(2):256–262CrossRefGoogle Scholar
  18. Gehlke CE, Biehl K (1934) Certain effects of grouping upon the size of the correlation coefficient in census tract material. J Am Stat Assoc 29(185A):169–170CrossRefGoogle Scholar
  19. Ghosh T, Powell RL, Elvidge CD, Baugh KE, Sutton PC, Anderson S (2010) Shedding light on the global distribution of economic activity. Open Geogr J 3:147–160CrossRefGoogle Scholar
  20. de Groot H, Marlet G, Teulings C, Vermeulen W (2015) Cities and the urban land premium. Edward Elgar, CheltenhamCrossRefGoogle Scholar
  21. Haartsen T, Huigen PPP, Groote P (2003) Rural Areas in the Netherlands. Tijdschr Econ Soc Geogr 94(1):129–136CrossRefGoogle Scholar
  22. van der Hejde P (2012) New urban centres in the Netherlands. Tijdschr Econ Soc Geogr 103(3):362–373CrossRefGoogle Scholar
  23. Henderson V, Storeygard A, Weil DN (2011) A bright idea for measuring economic growth. Am Econ Rev 101(3):194–199CrossRefGoogle Scholar
  24. Hsu FC, Baugh KE, Ghosh T, Zhizhin M, Elvidge CD (2015) DMSP-OLS radiance calibrated nighttime lights time series with intercalibration. Remote Sens (Basel) 7(2):1855–1876CrossRefGoogle Scholar
  25. Jiang B (2015) Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity. GeoJournal 80(1):1–13CrossRefGoogle Scholar
  26. Jiang B, Miao Y (2014) The evolution of natural cities from the perspective of location-based social media. Prof Geogr 67(2):295–306CrossRefGoogle Scholar
  27. Jiang B, Yin J, Liu Q (2015) Zipf’s law for all the natural cities around the world. Int J Geogr Inf Sci 29(3):498–522CrossRefGoogle Scholar
  28. Krugman P (1996) The self organizing economy. Blackwell, Cambridge (Mass.)Google Scholar
  29. Krugman P (1997) Development, geography and economic theory. MIT Press, Cambridge (Mass.)Google Scholar
  30. Liu Q (2014) A Case Study on the Extraction of the Natural Cities from Nightlight Image of the United States of America. Master thesis, University of GävleGoogle Scholar
  31. Malevergne Y, Pisarenko V, Sornette D (2011) Testing the Pareto against the lognormal distributions with the uniformly most powerful unbiased test applied to the distribution of cities. Phys Rev E 83(3):36111CrossRefGoogle Scholar
  32. Mellander C, Stolarick K, Matheson Z, Lobo J (2013) Night-time light data: a good proxy measure for economic activity? Martin Prosperity Institute, TorontoGoogle Scholar
  33. OECD (1994) Creating indicators for shaping territorial policy. OECD, ParisGoogle Scholar
  34. OECD (2013) Definition of Functional Urban Areas (FUA) for the OECD metropolitan database. OECD, ParisGoogle Scholar
  35. Pandiya M, Baxi A, Potdar MB, Kalubarme MH, Agarwal B (2013) Comparison of various classification techniques for satellite data. Int J Sci Eng Res 4(1):1–6Google Scholar
  36. Reggiani A, Nijkamp P (2015) Did Zipf anticipate Socio-economic Spatial Networks? Environ Plann B 42(3):468–489CrossRefGoogle Scholar
  37. Rosen KT, Resnick M (1980) The size and distribution of cities: an examination of Pareto law and primacy. J Urban Econ 8(2):165–186CrossRefGoogle Scholar
  38. Small C, Pozzi F, Elvidge CD (2005) Spatial analysis of global urban extent from DMSP-OLS night lights. Remote Sens Environ 96(3–4):277–291CrossRefGoogle Scholar
  39. Soo KT (2005) Zipf’s law for cities: a cross country investigation. Reg Sci Urban Econ 35(3):239–263CrossRefGoogle Scholar
  40. Starrett D (1978) Market allocations of location choice in a model with free mobility. J Econ Theory 17(1):21–37CrossRefGoogle Scholar
  41. Stefanovic IL, Scharper SB (eds) (2012) The natural city: re-envisioning the built environment. University of Toronto Press, TorontoGoogle Scholar
  42. Suedekum J, Giesen K (2011) Zipf’s law for cities in the regions and the country. J Econ Geogr 11(4):667–686CrossRefGoogle Scholar
  43. Sutton P (1998) Modelling population density with night-time satellite imagery and GIS. Comput Environ Urban Syst 21(3–4):227–244Google Scholar
  44. Vandervieren E, Huber M (2004) An adjusted box-plot for skewed distributions. In: Antoch J (ed) Compstat 2004 Symposium. Physica, Springer, Heidelberg, pp 1933–1940Google Scholar
  45. Wikipedia (2017) Gemeentelijke Herindelingen. https://nl.wikipedia.org/wiki/Gemeentelijke_herindelingen_in_Nederland. Accessed 18 Dec 2017Google Scholar
  46. Wu S (2015) Zipf’s Law for Natural Cities Extracted from Location-Based Social Media Data. Master thesis, University of GävleGoogle Scholar
  47. Yang W, Hou K, Liu B, Yu F, Lin L (2017) Two-stage clustering technique based on the neighboring union histogram for Hyperspectral remote sensing images. IEEE Access 5:5640–5647Google Scholar
  48. Zipf GK (1949) Human behaviour and the principles of least effort. Addison Wesley, New YorkGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.PRAC Bergs & Issa Partnership Co.Bad SodenGermany

Personalised recommendations