Shrinking cities on the globe: Evidence from LandScan 2000–2019

Shrinking cities have spread across the globe in recent decades, characterizing significant population loss, economic decline, and decay in spatial quality. To maintain global economic prosperity in the context of urban shrinkage and support decision making in the direction, it is necessary to accurately identify shrinking cities on a global scale. We utilize redefined natural city boundaries and the LandScan dataset to identify and map shrinking cities experiencing population loss on the globe. As a result, we have identified 5004 shrinking cities worldwide, with a total area of 126,930 km2 during 2000–2019. The ratio of which in number and in area is 27% and 22%, respectively. The shrinking cities are clustered and mainly located in Europe, Eastern Asia, and northeastern United States. There are 41 countries with more than 20 shrinking cities on the globe. The number of shrinking cities in China reached 679, which is the most. Among the 41 countries, the median value of the natural cities’ shrinking ratios of Iraq, Iran, Austria, South Africa, Russia, Georgia, and Belarus is >50%, indicating that the urban population loss in these countries is relatively serious. Our findings can be used to inform decision makers and urban planners to adjust the “growth-oriented” planning paradigm and adopt precise strategies, to form a healthier urban development.

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48,118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32,972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.

Living Structure as an Empirical Measurement of City Morphology

Human actions and interactions are shaped in part by our direct environment. The studies of Christopher Alexander show that objects and structures can inhibit natural properties and characteristics; this is measured in living structure. He also found that we have better connection and feeling with more natural structures, as they more closely resemble ourselves. These theories are applied in this study to analyze and compare the urban morphology within different cities. The main aim of the study is to measure the living structure in cities. By identifying the living structure within cities, comparisons can be made between different types of cities, artificial and historical, and an estimation of what kind of effect this has on our wellbeing can be made. To do this, natural cities and natural streets are identified following a bottom-up data-driven methodology based on the underlying structures present in OpenStreetMap (OSM) road data. The naturally defined city edges (natural cities) based on intersection density and naturally occurring connected roads (natural streets) based on good continuity between road segments in the road data are extracted and then analyzed together. Thereafter, historical cities are compared with artificial cities to investigate the differences in living structure; it is found that historical cities generally consist of far more living structure than artificial cities. This research finds that the current usage of concrete, steel, and glass combined with very fast development speeds is detrimental to the living structure within cities. Newer city developments should be performed in symbiosis with older city structures as a whole, and the structure of the development should inhibit scaling as well as the buildings themselves.

Multivariate thematic mapping using fractal glyphs

<p><strong>Abstract.</strong> Cartographers often face the task of depicting multivariate data on a single map. As spatial data gets ever more voluminous, and in particular, complex (multifarious), this challenge will have to be overcome with increasing regularity. Many solutions have been implemented in answer to situations like this: choropleths with trivariate colour or texture schemes, or point symbols in the form of star plots, ray glyphs or more naturalistically, Chernoff Faces (all of which could be sized proportional to a further variable).</p><p>Point symbols with a naturalistic basis have a mimetic appearance that makes for unambiguous communication of data. Chernoff faces in particular work so well due to their resemblance to, and human ability in reading, human faces. Importantly, facial features are easily linked with data through manipulation of their size, position or orientation (e.g. small to large nose; frowny to smily mouth).</p><p>Computer-generated fractals offer further potential solutions to the multivariate challenge in the naturalistic category. Examples such as the Barnsley fern leaf exhibit the fractal property of self-similar geometry over multiple spatial scales to create a realistic fern appearance. Fundamentally, the fern figure is under full control of a few numerical parameters that are simple to link with underlying attribute data. If these fractal glyphs are plotted as points on a map then we have a novel and potentially rich basis for multivariate mapping to address the big data challenge.</p><p>We can generate a Barnsley fern leaf fractal using the Iterated Function System (IFS), driven by parameters for geometric operators: displacement, rotation and scaling (Table 1). Each of these transforms can be linked with a variable, which is the basis for multivariate representation. Figure 1 illustrates how the appearance of the Barnsley fern is affected by scaling, overall orientation and angle of fern frond. In Table 1, cells 2a and d are used for scaling, 2b for orientation and 3d / 4d for frond angle.</p><p> Our demonstrating example is based on “natural cities” (and within them, “natural streets” that define the city outline, Figure 2a), which have been calculated through the head/tail breaks method (Jiang, 2013) to capture a size-based emergent organic hierarchy. For natural streets, street network junction points have been triangulated, and triangular edges divided into two groups, those longer than the mean edge length (the “head”) and those shorter (the “tail”). This process is recursively applied to the head, each time adding a tier to the street size hierarchy. Natural cities are calculated through a similar process, but on the basis of mean settlement area. In Figure 2b, natural city parameters have been used on the top 11 New Zealand cities in the following way: scaling has been linked to number of breaks (this is the ht-index, which indicates how complex the city network form is - Jiang and Yin, 2014), angle of fronds to percentage size of head and overall orientation to North (pointing right) and South Islands (left).</p><p> Rigorous testing needs to take place to measure the effect of fractal multivariate symbols compared with conventional cartographic means of representing the same data (which may include separate variable maps and the multivariate conventions listed above). Usability of fractally enhanced maps can be assessed along efficiency (how long it takes to perform a specific map task), effectiveness (how correct that task was performed) and satisfaction (covering ease, engagement and enjoyment of the map user in performing the map-based tasks).</p><p>Beyond multivariate mapping, these fractal glyphs are being investigated as one of the building blocks in generating data-based artworks (through the use of convolutional neural networks for style transfer – Moore and Jiang, 2017). This symbolisation based on self-similar graphics is an extension of a fractally-oriented vision for cartography (Jiang, 2018). It also addresses a part of the current cartography and Big Data agenda addressing spatial Big Data representation using artworks (Robinson et al, 2017).</p>

Natural Cities Generated from All Building Locations in America

Authorities define cities—or human settlements in general—through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city using the notion of natural cities and based on head/tail breaks, which is a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations—all building footprints of America (mainland) or their centroids more precisely—to generate 2.1 million natural cities in the country (see the URL as shown in the note of Figure 1). In contrast to government defined city boundaries, these natural cities constitute a valuable data source for city-related research.

Natural Cities Generated from All Building Locations in America

Authorities define cities &ndash; or human settlements in general &ndash; through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city based on the notion of natural cities that is defined based on head/tail breaks, a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations &ndash; all building footprints of America (mainland) or their centroids more precisely &ndash; to derive 2.1 million natural cities in the country (http://lifegis.hig.se/uscities/). These natural cities &ndash; in contrast to government defined city boundaries &ndash; constitute a valuable data source for city-related research.