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

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.ZusammenfassungWie 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.

[1]  Bin Jiang,et al.  The Evolution of Natural Cities from the Perspective of Location-Based Social Media , 2014, Digital Social Networks and Travel Behaviour in Urban Environments.

[2]  Sirui Wu Zipf's Law for Natural Cities Extracted from Location-Based Social Media Data , 2015 .

[3]  Christopher D. Elvidge,et al.  DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration , 2015, Remote. Sens..

[4]  Gilles Duranton,et al.  Urban Evolutions: The Fast, the Slow, and the Still , 2007 .

[5]  Kun Hou,et al.  Two-Stage Clustering Technique Based on the Neighboring Union Histogram for Hyperspectral Remote Sensing Images , 2017, IEEE Access.

[6]  P. Krugman The Self Organizing Economy , 1996 .

[7]  Peter Nijkamp,et al.  Did Zipf Anticipate Socio-Economic Spatial Networks? , 2012 .

[8]  J. Eeckhout Gibrat's Law for (All) Cities , 2004 .

[9]  Y. Yamagata,et al.  Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data , 2015 .

[10]  Kristian Giesen,et al.  Zipf's Law for Cities in the Regions and the Country , 2011, SSRN Electronic Journal.

[11]  Paul Krugman,et al.  Development, Geography, and Economic Theory , 1995 .

[12]  C. Teulings,et al.  Cities and the Urban Land Premium , 2015 .

[13]  J. Henderson,et al.  A Bright Idea for Measuring Economic Growth. , 2011, The American economic review.

[14]  C. Mellander,et al.  Night-Time Light Data: A Good Proxy Measure for Economic Activity? , 2015, PloS one.

[15]  Natural City Growth in the People's Republic of China , 2017 .

[16]  Peter Groote,et al.  Rural Areas in the Netherlands , 2003 .

[17]  Bin Jiang,et al.  Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity , 2015 .

[18]  Manoj Pandya,et al.  Comparison of Various Classification Techniques for Satellite Data , 2013 .

[19]  Bin Jiang,et al.  Zipf’s law for all the natural cities around the world , 2014, Int. J. Geogr. Inf. Sci..

[20]  P. Sutton,et al.  Shedding Light on the Global Distribution of Economic Activity , 2010 .

[21]  M. Lazar Shedding Light on the Global Distribution of Economic Activity , 2010 .

[22]  Qingli Liu A Case Study on the Extraction of the Natural Cities from Nightlight Image of the United States of America , 2013 .

[23]  X. Gabaix Zipf's Law and the Growth of Cities , 1999 .

[24]  J. A. Quintanilha,et al.  DMSP/OLS night‐time light imagery for urban population estimates in the Brazilian Amazon , 2006 .

[25]  NEW URBAN CENTRES IN THE NETHERLANDS , 2012 .

[26]  P. Sutton Modeling population density with night-time satellite imagery and GIS , 1997 .

[27]  C. Elvidge,et al.  Spatial analysis of global urban extent from DMSP-OLS night lights , 2005 .

[28]  X. Gabaix Zipf's Law for Cities: An Explanation , 1999 .

[29]  Dong Li,et al.  Is the Zipf law spurious in explaining city-size distributions? , 2006 .

[30]  Kenneth T. Rosen,et al.  The Size Distribution of Cities: An Examination of the Pareto Law and Primacy , 1980 .

[31]  Xavier Gabaix,et al.  Power Laws in Economics: An Introduction , 2016 .

[32]  Rolf Bergs,et al.  Cross-border Cooperation, Regional Disparities and Integration of Markets in the EU , 2012 .

[33]  X. Gabaix,et al.  Rank − 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents , 2007 .

[34]  Peter Nijkamp,et al.  Did Zipf Anticipate Spatial Connectivity Structures? , 2015 .

[35]  C. E. Gehlke,et al.  Certain Effects of Grouping upon the Size of the Correlation Coefficient in Census Tract Material , 1934 .

[36]  Steven Brakman,et al.  The Return of Zipf: Towards a Further Understanding of the Rank‐Size Distribution , 1999 .

[37]  Christopher D. Elvidge,et al.  Area and position accuracy of DMSP nighttime lights data , 2004 .

[38]  A. Venables,et al.  The Spatial Economy: Cities, Regions, and International Trade , 1999 .

[39]  Yannick Malevergne,et al.  Testing the Pareto against the lognormal distributions with the uniformly most powerful unbiased test applied to the distribution of cities. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  David A. Starrett,et al.  Market allocations of location choice in a model with free mobility , 1978 .

[41]  K. Soo Zipf's law for cities: a cross-country investigation , 2005 .