skip to main content


Title: Automated identification of urban substructure for comparative analysis
Neighborhoods are the building blocks of cities, and thus significantly impact urban planning from infrastructure deployment to service provisioning. However, existing definitions of neighborhoods are often ill suited for planning in both scale and pattern of aggregation. Here, we propose a generalized, scalable approach using topological data analysis to identify barrier-enclosed neighborhoods on multiple scales with implications for understanding social mixing within cities and the design of urban infrastructure. Our method requires no prior domain knowledge and uses only readily available building parcel information. Results from three American cities (Houston, New York, San Francisco) indicate that our method identifies neighborhoods consistent with historical approaches. Additionally, we uncover a consistent scale in all three cities at which physical isolation drives neighborhood emergence. However, our methods also reveal differences between these cities: Houston, although more disconnected on larger spatial scales than New York and San Francisco, is less disconnected at smaller scales.  more » « less
Award ID(s):
1941695
NSF-PAR ID:
10297396
Author(s) / Creator(s):
; ;
Editor(s):
Lenormand, Maxime
Date Published:
Journal Name:
PLOS ONE
Volume:
16
Issue:
1
ISSN:
1932-6203
Page Range / eLocation ID:
e0245067
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    We examined the effect of social distancing on changes in visits to urban hotspot points of interest. In a pandemic situation, urban hotspots could be potential superspreader areas as visits to urban hotspots can increase the risk of contact and transmission of a disease among a population. We mapped census-block-group to point-of-interest (POI) movement networks in 16 cities in the United States. We adopted a modified coarse-grain approach to examine patterns of visits to POIs among hotspots and non-hotspots from January to May 2020. Also, we conducted chi-square tests to identify POIs with significant flux-in changes during the analysis period. The results showed disparate patterns across cities in terms of reduction in hotspot POI visitors. Sixteen cities were divided into two categories using a time series clustering method. In one category, which includes the cities of San Francisco, Seattle and Chicago, we observed a considerable decrease in hotspot POI visitors, while in another category, including the cities of Austin, Houston and San Diego, the visitors to hotspots did not greatly decrease. While all the cities exhibited overall decreased visitors to POIs, one category maintained the proportion of visitors to hotspot POIs. The proportion of visitors to some POIs (e.g. restaurants) remained stable during the social distancing period, while some POIs had an increased proportion of visitors (e.g. grocery stores). We also identified POIs with significant flux-in changes, indicating that related businesses were greatly affected by social distancing. The study was limited to 16 metropolitan cities in the United States. The proposed methodology could be applied to digital trace data in other cities and countries to study the patterns of movements to POIs during the COVID-19 pandemic. 
    more » « less
  2. Abstract

    A growing number of cities are investing in green infrastructure to foster urban resilience and sustainability. While these nature-based solutions are often promoted on the basis of their multifunctionality, in practice, most studies and plans focus on a single benefit, such as stormwater management. This represents a missed opportunity to strategically site green infrastructure to leverage social and ecological co-benefits. To address this gap, this paper builds on existing modeling approaches for green infrastructure planning to create a more generalizable tool for comparing spatial tradeoffs and synergistic ‘hotspots’ for multiple desired benefits. I apply the model to three diverse coastal megacities: New York City, Los Angeles (United States), and Manila (Philippines), enabling cross-city comparisons for the first time. Spatial multi-criteria evaluation is used to examine how strategic areas for green infrastructure development across the cities change depending on which benefit is prioritized. GIS layers corresponding to six planning priorities (managing stormwater, reducing social vulnerability, increasing access to green space, improving air quality, reducing the urban heat island effect, and increasing landscape connectivity) are mapped and spatial tradeoffs assessed. Criteria are also weighted to reflect local stakeholders’ desired outcomes as determined through surveys and stakeholder meetings and combined to identify high priority areas for green infrastructure development. To extend the model’s utility as a decision-support tool, an interactive web-based application is developed that allows any user to change the criteria weights and visualize the resulting hotspots in real time. The model empirically illustrates the complexities of planning green infrastructure in different urban contexts, while also demonstrating a flexible approach for more participatory, strategic, and multifunctional planning of green infrastructure in cities around the world.

     
    more » « less
  3. Abstract

    Urbanization negatively impacts water quality in streams by reducing stream‐groundwater interactions, which can reduce a stream's capacity to naturally attenuate nitrate. Meadowbrook Creek, a first order urban stream in Syracuse, New York, has an inverse urbanization gradient, with heavily urbanized headwaters that are disconnected from the floodplain and downstream reaches that have intact riparian floodplains and connection to riparian aquifers. This system allows assessment of how stream‐groundwater interactions in urban streams impact the net sources and sinks of nitrate at the reach scale. We used continuous (15‐min) streamflow measurements and weekly grab samples at three gauging stations positioned longitudinally along the creek to develop continuous nitrate load estimates at the inlet and outlet of two contrasting reaches. Nitrate load estimates were determined using a USGS linear regression model, RLOADEST, and differences between loads at the inlet and outlet of contrasting reaches were used to quantify nitrate sink and source behaviour year‐round. We observed a nitrate load of 1.4 × 104 kg NO3per water year, on average, at the outlet of the urbanized reach while the nitrate load at the outlet of the downstream, connected reach was 1.0 × 104 kg NO3per water year, on average. We found the more heavily urbanized, hydrologically‐disconnected reach was a net source of nitrate regardless of season. In contrast, stream‐groundwater exchange caused the hydrologically connected reach to be both a source and sink for nitrate, depending on time of year. Both reaches alter nitrate source and sink behaviour at various spatiotemporal scales. Groundwater connection in the downstream, connected reach reduces annual nitrate loads and provides more opportunities for sources and sinks of nitrate year‐round than the hydrologically disconnected stream reach. Mechanisms include groundwater discharge into the stream with variable nitrate concentrations, surface‐water groundwater interactions that foster denitrification, and stream load loss to surrounding near‐stream aquifers. This study emphasizes how loads are important in understanding how stream‐groundwater interactions impact reach scale nitrate export in urban streams.

     
    more » « less
  4. Abstract

    Urbanization significantly alters natural ecosystems and has accelerated globally. Urban wildlife populations are often highly fragmented by human infrastructure, and isolated populations may adapt in response to local urban pressures. However, relatively few studies have identified genomic signatures of adaptation in urban animals. We used a landscape genomic approach to examine signatures of selection in urban populations of white‐footed mice (Peromyscus leucopus) in New York City. We analysed 154,770SNPs identified from transcriptome data from 48P. leucopusindividuals from three urban and three rural populations and used outlier tests to identify evidence of urban adaptation. We accounted for demography by simulating a neutralSNPdata set under an inferred demographic history as a null model for outlier analysis. We also tested whether candidate genes were associated with environmental variables related to urbanization. In total, we detected 381 outlier loci and after stringent filtering, identified and annotated 19 candidate loci. Many of the candidate genes were involved in metabolic processes and have well‐established roles in metabolizing lipids and carbohydrates. Our results indicate that white‐footed mice in New York City are adapting at the biomolecular level to local selective pressures in urban habitats. Annotation of outlier loci suggests selection is acting on metabolic pathways in urban populations, likely related to novel diets in cities that differ from diets in less disturbed areas.

     
    more » « less
  5. Hart, John P. (Ed.)
    Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization. 
    more » « less