Identifying urban areas: A new approach and comparison of national urban metrics with gridded population data

The measurement of urbanization and other key urban indicators depends on how urban areas are defined. The Degree of Urbanization (DEGURBA) has been recently adopted to support international statistical comparability, but its rigid criteria for classify areas as urban/non-urban based upon fixed population size and density criteria is controversial. Here we present an alternative approach to urban classification, using a flexible range of population density \& count thresholds. We then compare how these thresholds affect estimation of urbanization and urban settlement counts across three of the most popular gridded population datasets (GPD). Instead of introducing further uncertainties by matching GPD to built-up area datasets, we classify urban areas in a purely spatial demographic way. By calculating national urban shares and urban area counts, we highlight the often overlooked uncertainties when using GPD. We find that the choice of GPD is generally the dominant factor in altering both of these urban indicators but the choice of urban criteria is also important. Overall, this alternative urban classification method offers a more flexible approach to human settlements classification that can be applied globally for comparative research.

Machine learning for buildings’ characterization and power-law recovery of urban metrics

In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our knowledge to the near-city-scale. Neural networks and random forests are applied to identify the buildings’ number of floors and construction periods’ dependencies on a set of shape features: area, perimeter, and height along with the annual electricity consumption, relying a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our workflow.

New residential perennial floras reflect distinctive decisions of residential developers and new homeowners

Urban patterns reflect the people who build and manage urban space. However, most field research on residential vegetation focuses on household or neighborhood preferences, norms, or socioeconomic drivers of observed patterns and plant traits. Very few urban ecology researchers have studied residential real estate developers, who configure the space and establish the initial plant communities. How do the landscaping decisions of developers and homeowners shape residential perennial floras? To answer this question, I collected a stratified random sample of perennials at 60 newly built and sold homes in the Seattle, WA area. Through field sampling, conversations with new homeowners, and archival research, I assigned each individual perennial to one of three origins: remnant, planted by developers, or planted by homeowners. After describing landscaping decisions using plant traits (as presented in gardening literature), I evaluated whether planting decisions of developers and homeowners were heterogeneous and whether urban form or economic drivers influenced planted species richness. I also tested whether homeowner yard and plant buying preferences could be linked to planted richness. Given that developers and homeowners have different incentives, I hypothesized that they would choose different types of perennials and that urban metrics related to area and economics would increase species richness. I also predicted that homeowner preferences would be linked to species richness patterns. Developers planted most of the trees, shrubs, and graminoids. Homeowners planted fewer woody and more herbaceous perennial species. Parcel planting area, wealth related metrics, and parcel density increased species richness for some perennials. However, homeowner preferences were stronger predictors of their planting behavior than urban metrics. Because assembly of residential perennial flora communities is heterogeneous, future investigations in other urban ecosystems should incorporate preferences of developers and homeowners, site-specific constraints, and broader scale influences. More work is needed to understand developer incentives and preferences.

Innovating Metrics for Smarter, Responsive Cities

This paper explores the emerging and evolving landscape for metrics in smart cities in relation to big data challenges. Based on a review of the research literature, the problem of “synthetic quantitative indicators” along with concerns for “measuring urban realities” and “making metrics meaningful” are identified. In response, the purpose of this paper is to advance the need for innovating metrics for smarter, more interactive and responsive cities in addressing and mitigating algorithmic-related challenges on the one hand, and concerns associated with involving people more meaningfully on the other hand. As such, the constructs of awareness, learning, openness, and engagement are employed in this study. Using an exploratory case study approach, the research design for this work includes the use of multiple methods of data collection including survey and interviews. Employing a combination of content analysis for qualitative data and descriptive statistics for quantitative data, the main findings of this work support the need for rethinking and innovating metrics. As such, the main conclusion of this paper highlights the potential for developing new pathways and spaces for involving people more directly, knowingly, and meaningfully in addressing big and small data challenges for the innovating of urban metrics.

Exploring the relationships between urban form metrics and the vegetation biomass loss under urban expansion in China

Compact urban form has been applied as a strategy to reduce the loss of green space that occurs from development, but the impact of this policy on the provision of green space still presents many uncertainties. This research investigated the statistical relationship between urban form indicators and the loss of vegetation biomass to understand the response of quality green space provision to changes in urban morphology. A methodology combining multi-source data assimilation, statistical analysis, and spatial analysis was adopted for the Yangtze River Delta cities of China. First, six urban metrics were selected to describe the shape and layout of urban patches in each city, and the total biomass loss index was then introduced as a parameter. The values of urban metrics and total biomass loss index were calculated for the 50 Yangtze River Delta cities. Second, ordinary least squares regression and geographically weighted regression analyses were then used to establish a quantitative relationship between total biomass loss index and urban form indicators. The results revealed an extremely negative correlation between total biomass loss index and the three urban variables of Richard compactness, density gradient, and the Gini coefficient; moreover, the parameter estimates for the three variables in the geographically weighted regression model were local and varied over space. Third, the mechanisms by which the urban form influences biomass loss were discussed and different urban form planning strategies for particular urban areas were suggested. In conclusion, compact urban form in a clustered layout of urban areas with a dense central agglomeration was verified to be ecologically superior and conducive to green space protection. For the physical interpretation of the statistical relationship between urban morphology and vegetation loss, the interface effect of urban agglomeration on vegetation merits further study.