Urban big data analytics and morphology

City comparison at different spatial and temporal scales

Taubenböck et al. (p. 1206) carry out a cross-sectional and longitudinal analysis of urban land use patterns of 230 city regions in 34 European countries (111 cities in the Western region and 119 cities in the Eastern region). The study is based on several recently available global datasets: the Gridded Population of the World database, the Global Urban Footprint mapping product for the year 2012 and mapping products based on optical Landsat sensors at four points in time (1975, 1990, 2000 and 2010). The consistent and valid comparison of settlement patterns leads to the conclusion that Eastern cities and city regions carry typical features of capitalist urbanisation, but that relics of the socialist past are also ubiquitous.

Berghauser Pont et al. (p. 1226) develop typologies of urban form taking into account variables for streets, plots and buildings in five European cities (Amsterdam, London, Stockholm, Gothenburg and Eskilstuna in Sweden). The authors focus on the test of two well-known theoretical propositions in urban morphology: the concept of the burgage cycle and the theory of natural movement. Finally, a discussion is carried out on the future development of cities and the associated normative guidelines.

Analysis of the urban fabric and building stocks

Araldi et al. (p. 1243) propose a new multi-step geoprocessing approach for the analysis of the urban fabric as a fundamental small-scale component of urban form. The approach consists of a matrix of morphological indicators taking into account the pedestrian perspective, network-constrained geo-statistics and Bayesian clustering. Finally, nine types of urban fabric are presented for the French Riviera area as a test bed.

Yang et al. (p. 1264) use official statistical data, new online data and proprietary digital data to create a dynamic spatial equilibrium model to understand past experiences and future options for the development of new urban sub-centres in Shanghai. The study aims to explore possible polycentric development scenarios (2010–2035). It makes a contribution to the decision-making processes of intra-city sub-centre development and could be useful in many developing countries.

Bradley and Behnisch (p. 1281) investigate German building stock data. Event histories of a building stock in South-West Germany as well as the number of buildings in Germany’s municipalities and counties follow a heavy-tailed distribution. Zipf’s law is identified as the true model in the first two cases, whereas a log-normal distribution is supported in the latter case. Heavy-tailed distributions have the property that studying the effects of a few large values already yields most of the overall effect of the whole quantity.

Influence of spatial configuration on human spatial behaviour using big data

Ma et al. (p. 1297) conclude that topological representation with little metric information tends to capture a majority of human activities, whereas segment analysis does not. They focus on human activities, which are, in particular, characterised by the scaling of numerous least-connected streets, very few most-connected and some in between the least- and most-connected. The authors point out that the best spatial representation in terms of human activities or traffic prediction is given by natural roads followed by axial lines, and that neither street segments nor line segments can capture Tweet locations well. It should be noted that any spatial cognition resulting from spatial syntax or topological analysis is determined by space and not by humans. Future work should aim to develop new means for urban planning and design.

Guerrero Balarezo et al. (p. 1314) present a crowdsourced big data approach to investigate the influence of the spatial configuration on human spatial behaviour, particularly on active mobility in Cuenca, Ecuador. Using space syntax as a tool for characterising morphology, the authors try to close a gap in this relationship in the Latin American context.

Hellervik et al. (p. 1331) highlight the importance of accessibility, which determines the geographical distribution of urban activities. A network centrality measure is proposed to predict the distribution of urban activity from the structure of infrastructure networks. Gothenburg is used as a prototype for development, testing and validation. OpenStreetMap is used to identify roads and streets with preserved topology and attributes. Activity, accessibility and attractiveness are unified in the approach, which forms a basis for new modelling applications.

Big data and urban mobility

Garnica-Monroy et al. (p. 1347) combine two accessibility indices (cumulative opportunities and Space Syntax’s integration value) in order to assess the degree to which the lack of a basic public service could be associated with the spatial segregation of an area. The 22 largest metropolitan areas of Mexico serve as the study area. The authors conclude that evidence-based studies should be taken into account by policy makers to improve the quality of life for the residents by providing basic urban facilities while reducing spatial inequalities.

The study of Blumenfeld et al. (p. 1362) uses big data on urban traffic flow and identifies functional dynamic areas in the city centre of London and Tel Aviv. Two types of clusters are distinguished: temporal clusters, which represent the traffic flows at a given point in time, and clusters, which represent the changes in the traffic flow over time. The approach provides the basis for new adpative tools for planning that integrate considerations concerning public well-being or the quality of urban life.