Environment and Planning B: Urban Analytics and City Science

We explore regional and urban clusters and patterns in Europe by using satellite images of nighttime lights and by employing Exploratory Spatial Data Analysis. We map Defense Meteorological Satellite Program nighttime lights data onto the nomenclature of territorial units for statistics III, Local Administrative Units II and pixel (i.e. 1 km² grid cell system of Europe) level and apply global and local statistics of spatial association. Under the assumption that nighttime light data are a good proxy for economic activity, the analysis at regional level shows that the regions of global cities and megacities and their surrounding areas are hot spots of high economic activity levels. The regional analysis also reveals the polycentric hierarchical structure of Europe. Using the case studies of the regions of London and Île-de-France, the analysis at the urban level reveals the different urban structure of these two global regions and identifies the functional urban areas of London and Paris.

This paper proposes non-linear asymmetric gap–satisfaction models to assess the influence of the gap between aspirations and perceived residential attributes on residential satisfaction. Two variants of the residential gap are specified based on the difference and ratio between aspiration and reality. Besides the influence of residential gap, the interactions with social-demographics are incorporated in three specifications of gap–satisfaction relationship. Using empirical data collected from eight renovated historical blocks in two Chinese cities, the relative performances of the proposed gap models are compared with truncated linear symmetric gap models and traditional linear (absolute) difference models for housing, living environmental and neighbourhood attributes. The estimation results indicate that overall the non-linear asymmetric gap models outperform the linear gap models. The model fit for housing and living environmental attributes are very good. Satisfaction for housing attributes is predicted best with non-linear asymmetric gap ratio models, while the environmental attributes are best represented by non-linear asymmetric difference models. In case of the neighbourhood dimension, non-linear asymmetric gap ratio models show the best performance, while these models yield a good fit only for two attributes. The interaction effect of social-demographics is found to vary between residential attributes.

OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales—namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

The interaction between environmental noise and the built environment is an often-neglected area in the practice of urban planning and design. Most quantitative research is limited to single value loudness metrics and ignores the more complex spatial nuances of the noisescape. Qualitative soundscape research, on the other hand, is difficult to generalize to the urban scale. We report on an exploratory noise sensing project in Los Angeles, CA that investigates both qualitative and quantitative aspects of the noisescape. Using an experimental array of noise sensors mounted on city street lights, we collected preliminary data that demonstrate the promising and revealing nature of spatially and temporally granular urban sound data. By analyzing sounds in various frequency bands at different resolutions, we investigate how aspects of urban design such as landscaping, material choice, and building typologies impact the sonic environment. Our results reveal the spatio-temporal structure of low-frequency noise in traffic-exposed areas; a phenomenon not captured by traditional A-weighted decibel metrics. Based on these results, we present a model predicting noise based on historic traffic data. These results provide insights for future methods that can be applied to long-term policymaking and planning decisions.

Gated communities have become a common feature in recent decades and have been shown to lead to social inequality to the detriment of the poorest social classes. Because access to urban green space is crucial for both physical health and spiritual pleasure, it is often regarded as an indicator of social justice; however, there are many references to the current inequity in urban green space accessibility. Our study aimed first to measure the potential spatial accessibility of green space in the central urban area of Beijing; then to evaluate the socio-economic disparities in green space accessibility; and finally, to assess the effect of the policy of “opening up gated residential communities” on urban green space accessibility. We adopt the Gaussian-based two-step floating catchment to assess the spatial accessibility of green spaces in each residential zone in the central area of Beijing, and the ordinary least squares model was used to evaluate the inequity in accessibility caused by socio-economic disparities. The results reveal that lower income residential zones have remarkably lower access to green spaces. Next, by comparing the differences in accessibility equity between two comparable scenarios in which all communities have dismantled their fences, we unexpectedly find that the inequity of access to urban green space does not improve but becomes more pronounced. We attribute this result to socio-spatial polarization. Our findings can be used by urban planners to target current urban planning system reform and by policymakers to focus closely on the gradual spatial polarization between the rich and the poor.

We develop a ‘multifocal’ approach to reveal spatial dissimilarities in cities, from the most local scale to the metropolitan one. Think, for instance, of a statistical variable that may be measured at different scales, e.g. ethnic group proportions, social housing rate, income distribution, or public transportation network density. Then, to any point in the city there corresponds a sequence of values for the variable, as one zooms out around the starting point, all the way up to the whole city – as if with a varifocal camera lens. The sequences thus produced encode spatial dissimilarities in a precise manner: how much they differ from perfectly random sequences is indeed a signature of the underlying spatial structure. We introduce here a mathematical framework that allows to analyse this signature, and we provide a number of illustrative examples.

The Dutch concept of ‘bicycle highways’ is increasingly being adopted by urban planners owing to rising environmental and health consciousness, and the growing popularity of electric bicycles. Bicycle highways differ from other types of cycling infrastructure in that they avoid intersections with motorised traffic, and are wide enough to allow for safe overtaking, thereby increasing cycling speeds. While many studies investigate the feasibility of constructing bicycle highways, few explore their effect on users’ travel preferences. In this context, our study aims to assess the potential impact of bicycle highways on commuter mode choice. We built a discrete choice model based on individual commute data from a national household travel survey, Mobilität in Deutschland 2008. The model was estimated in a logit modelling framework using Biogeme. We estimated multinomial logit and nested logit models and found nested logit to be more appropriate. The model estimates were then applied to forecast mode shares in scenarios with the pilot bicycle highway proposed in the Munich region. The variation in mode shares across scenarios with increasing average cycling speeds was analysed in areas with varying proximity to the infrastructure. The results suggest that bicycle highways reduce motorised travel and increase cycling. The effect is stronger as proximity to the corridor increases. The analysis helps to quantify the potential impact of bicycle highways on commuter mode choice even without considering further benefits beyond travel time reductions, such as increased safety, convenience, comfort, and reduced risks due to fewer interactions with motorised traffic.

Urban metabolism studies have gained momentum in recent years as a means to assess the environmental performance of cities and to point to more resource-efficient strategies for urban development. Recent literature reviews report a growing number of applications of the industrial ecology model for material flow analysis in the design of the built environment. However, applications of material flow analysis in green infrastructure development are scarce. In this article, we argue that: (i) the use of material flow analysis in green infrastructure practice can inform decision-making towards more resource-efficient urban planning; (ii) the ecosystem service concept is critical to operationalize material flow analysis for green infrastructure planning and design, and, through this, can enhance the impact of urban metabolism research on policy making and planning practice. The article draws from a systematic review of literature on urban ecosystem services and benefits provided by green infrastructure in urban regions. The review focuses on ecosystem services that can contribute to a more energy-efficient and less carbon-intensive urban metabolism. Using the Common International Classification of Ecosystem Services as a baseline, we then discuss opportunities for integrating energy provision and climate regulation ecosystem services in material flow analysis. Our discussion demonstrates that the accounting of ecosystem services in material flow analysis enables expressing impacts of green infrastructure on the urban energy mix (renewable energy provision), the magnitude of energy use (mitigation of building energy demand) and the dynamics of biogeochemical processes in cities (carbon sequestration). We finally propose an expanded model for material flow analysis that illustrates a way forward to integrate the ecosystem service concept in urban metabolism models and to enable their application in green infrastructure planning and design.

While there has been no shortage of discussion of urban big data, smart cities, and cities as complex systems, there has been less discussion of the implications of big data as a source of individual data for planning and social science research. This study takes advantage of increasingly available land parcel and business establishment data to analyze how the measurement of proximity to urban services or amenities performed in many fields can be impacted by using these data—which can be considered “individual” when compared to aggregated origins or destinations. We use business establishment data across five distinctive US cities: Long Beach, Irvine, and Moreno Valley in California; Milwaukee, Wisconsin; and the New York borough of Staten Island. In these case studies, we show how aggregation error, a previously recognized concern in using census-type data, can be minimized through careful choice of distance measures. Informed by these regions, we provide recommendations for researchers evaluating the potential risks of a measurement strategy that differs from the “gold standard” of network distance from individually measured, point-based origins and destinations. We find limited support for previous hypotheses regarding measurement error based on the abundance or clustering of urban services or amenities, though further research is merited. Importantly, these new data sources reveal vast differences across cities, underscoring how accurate proximity measurement necessitates a critical understanding of the nuances of the urban landscape under investigation as measures appear heavily influenced by a city’s street layouts and historical development trajectories.

This paper sets out the results of a study conducted in the inland areas in central Italy on certain phenomena that, for decades, have evolved separately: urban and socio-economic growth, environmental and cultural conservation and the curbing of seismic risk. The study was carried out by analysing the urban conversion of land in the Italian Apennines over the past 50 years, focusing on areas of varying seismic hazard. Our analysis highlights that territorial planning has failed to tackle this risk in an integrated manner, implementing entirely uncoordinated actions that have produced poor results. Thus, our main goal is to study urban development and its effects on the Apennine system and devise possible strategies to mitigate the seismic risk in this area of significant worth, but made extremely vulnerable by policies and solutions that have never been “nature-based”.