Delineation of cities based on scaling properties of urban patterns: a comparison of three methods

Abstract

Identifying urban boundaries involves analysing both the functional and the morphological aspects of urban systems. In this paper, we adopt a purely morphological approach and compare three methods for the morphological delineation of cities. Each method avoids using any predefined quantified threshold (size, distance, built density, etc.) to detect crucial discontinuities in space. The first method identifies Natural Cities by clustering points. The other two are the fractal-based MorphoLim method and the Hierarchical Percolation; both involve transforming the data using a step-by-step dilation process. The three methods are critically compared and illustrated by applications to theoretical urban patterns. We further apply each method to the urban agglomeration of Brussels, the Belgian capital, using different data (building footprints, building centroids and street nodes) and considering two study areas, namely the former province of Brabant and the entire country of Belgium. The results show that it is impossible to draw an unambiguous morphological boundary for an urban agglomeration. Consequently, it is crucial to relate the data used, the size of the study area and the method chosen to the objectives of the delineation.

Urban Boundary Demarcation—An iCN Model Approach

“…Apart from the above-mentioned satellite-imagery-based urban boundary classification technics, density (i.e., either population or building) has been a frequently utilized approach to demarcate the urban boundaries of cities (Montero et al 2021). In such a context, Arcaute and her colleagues (Arcaute et al 2015) utilized population density distribution of "wards" to capture the urban boundaries of cities in the UK.…”

“…When calculating the population density of a wider administrative area, this method causes an underestimation of the urban boundaries of cities, resulting in a lower population density for the particular administrative unit, regardless of its urban functionality (Dijkstra et al 2021). Additionally, at present, it is well established that the administrative demarcations hardly represent the actual picture of the cities' urbanization process and their urban boundary, as it is purposefully utilized for administrative purposes (Montero et al 2021), (Dong et al 2015).…”

“…I. This method utilized raster population layers to initiate the Kernel Density, hence the accuracy of the derived urban boundaries is limited to the cell size of the utilized population raster later (Montero et al 2021), (Tian et al 2005). II.…”

“…This mainly caused the population census to be carried out every decade according to the administrative zones and then to downscale the population according to the ancillary information (Nicolau et al 2019), (Mennis 2003). Hence, it does not incorporate the complex and dynamic spatial changes of cities to the population distribution (Montero et al 2021), (Eicher and Brewer 2001). As a result, the applicability of the density approach to defining the city boundaries resulted in substantial model constraints.…”

Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery

“…Both NTL and OSM data help researchers construct alternative modeling units for spatial analyses at both intercity and intracity levels, and remove the barriers of inter-regional incomparability. The most recent relevant studies are so-called natural cities, referring to the objectively defined cities based on different types of urban elements from the open data, such as building footprints, street nodes, and points of interest (e.g., [25][26][27][28][29]). However, most of these studies take the derived cities as a whole to understand the scaling structure over a region or country, but seldom calibrate a "local" understanding of such spatial configuration at the intracity level.…”