Downtown vibrancy influences public health and safety outcomes in urban counties

Braun, Lindsay M.; Malizia, E

doi:10.1016/j.jth.2015.08.005

Cited by 43 publications

(31 citation statements)

References 22 publications

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“…Next, we applied equity analysis in downtown micro-scale walkability values to answer the second question regarding the horizontal inequities of walkability distribution within European downtown areas. This question is crucial for urban sustainability; studies from the United States have demonstrated that downtown vibrancy is related to more population-level health and safety outcomes [27]. Therefore, we found that in all investigated European urban cores, walkability is highly unequally distributed among downtown residents, where in some cases (Brussels, Warsaw and Athens), one-fourth of the population receives about 75% of the total walkability scores.…”

Section: Equity Analysis Resultsmentioning

confidence: 71%

“…Drawing upon previous MAPS research, we develop a short, online and massive segmentbased walkability data collection method in eight different European downtown districts. Additional studies [27] from the United States have provided evidence that vibrant and walkable downtowns cause better mobility and health outcomes in the city. In this manner, we aim to quantify a micro-scale walkability indicator and, in turn, to reveal any potential walkability inequities among downtown residents.…”

Section: Introductionmentioning

confidence: 99%

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Is walkability equally distributed among downtowners? Evaluating the pedestrian streetscapes of eight European capitals using a micro-scale audit approach

Bartzokas-Tsiompras¹,

Tampouraki²,

Photis³

2020

Int. J. TDI

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In this paper, we evaluate different elements of the urban micro-scale environment in eight European capitals' downtown areas (i.e. Vienna, Copenhagen, Warsaw, Madrid, Brussels, Budapest, Athens and Sofia) to provide insight into inequalities in walkability benefits due to spatial distribution. To this end, we utilize MAPS-Mini, the brief version of Microscale Audit of Pedestrian Streetscapes to record and assess, at the street level, 15 walkability related items based on the Google Street View service. Our total sample consists of about 15.736 street segments/crossings, while for reliability analysis reasons, a second rater was employed to cross assess 10% of street segments per city. Results showed that Vienna and Athens had the highest (50.4%) and lowest (32.1%) overall walkability scores, respectively. Assessments were further combined with the population estimates of the European Urban Atlas 2012 dataset to perform equity analysis by estimating the distribution of average walkability scores among the population living downtown in the examined cities. In doing so, we used the Gini (G.) index and constructed Lorenz curve graphs. Our findings reveal a landscape of high inequality in downtown walkability distribution since all Gini coefficients were higher than 0.43. However, the inequality was greatest in Brussels (G. = 0.60) and lowest in Budapest (G. = 0.43). Additionally, we used spatial statistics tests (i.e. global and local Moran's I) to identify global and local patterns of walkability and population. The results indicated a highly clustered pattern of walkability across all downtowns and designated several clusters of uneven walkability geographies. Our approach sheds light on the application of active mobility strategies in different European cities, highlighting at the same time the need for further research to provide a clearer assessment of the spatial distribution of inequalities in social benefits and impact when designing sustainable urban neighbourhoods.

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Section: Equity Analysis Resultsmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Is walkability equally distributed among downtowners? Evaluating the pedestrian streetscapes of eight European capitals using a micro-scale audit approach

Bartzokas-Tsiompras¹,

Tampouraki²,

Photis³

2020

Int. J. TDI

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“…In addition to the number of people in a location, other aspects are also being used to evaluate urban vibrancy: accessibility and connectivity [12], night-time light data [13], housing prices [14], and built-environment attributes [15]. Therefore, in studying urban vibrancy, the first key issue is to find a suitable proxy to precisely measure it and, consequently, to select a suitable dataset.…”

Section: Urban Vibrancymentioning

confidence: 99%

Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

2020

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Urban vibrancy is defined and measured differently in the literature. Originally, it was described as the number of people in and around streets or neighborhoods. Now, it is commonly associated with activity intensity, the diversity of land-use configurations, and the accessibility of a place. The aim of this paper is to study urban vibrancy, its relationship with neighborhood services, and the real estate market. Firstly, it is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city. Secondly, the influence of urban vibrancy on the listing prices of existing housing is analyzed by performing spatial analyses. To achieve this, the presence of spatial autocorrelation is investigated and spatial clusters are identified. Therefore, spatial autoregressive models are applied to manage spatial effects and to identify the variables that significantly influence the process of housing price determination. The results confirm that housing prices are spatially autocorrelated and highlight that housing prices and NeSI are statistically associated with each other. The identification of the urban areas characterized by different levels of vibrancy and housing prices can effectively support the revision of the urban development plan and its regulatory act, as well as strategic urban policies and actions. Such data analyses support a deep knowledge of the current status quo, which is necessary to drive important changes to develop more efficient, sustainable, and competitive cities.

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“…Wang and Guldmann [10] used a combination of population and employment density to measure urban vitality. Braun and Malizia [11] created an urban vibrancy index by compositing compactness, density, regional and local connectivity, destination accessibility, land use mix, and social diversity. Other measurements of urban vitality have included nighttime light data [12], population census data [13], and land price [14].…”

Section: Urban Vitalitymentioning

confidence: 99%

Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China

Han

Zhu

2019

Sustainability

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This study investigates the association between urban vitality and street centrality in Wuhan, China. Urban vitality was measured with social network review data. Street centrality was evaluated in terms of closeness, straightness, and betweenness in walking and driving mode. We constructed a square mesh to convert datasets of street centrality (segments) and urban vitality (points) into one unit for analysis. Geospatial visualization, a chi-square test, and correlation analysis were first employed to obtain an initial understanding of the spatial coupling relationship between urban vitality and street centrality. Then spatial regression models were applied to evaluate the significances and directions of the influences of street centrality on urban vitality. A geographical detector technique was utilized to further evaluate the magnitudes of these influences. The results suggest that street centrality plays an important role in shaping the spatial organization of urban vitality, and various street centralities presented marked differences in their association with urban vitality. More specifically, when considering street centrality in walking mode, betweenness affected urban vitality the most, followed by closeness and straightness. When considering street centrality in driving mode, straightness had the greatest influence on urban vitality, followed by closeness and betweenness.

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Downtown vibrancy influences public health and safety outcomes in urban counties

Cited by 43 publications

References 22 publications

Is walkability equally distributed among downtowners? Evaluating the pedestrian streetscapes of eight European capitals using a micro-scale audit approach

Is walkability equally distributed among downtowners? Evaluating the pedestrian streetscapes of eight European capitals using a micro-scale audit approach

Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

Exploring the Relationship between Urban Vitality and Street Centrality Based on Social Network Review Data in Wuhan, China

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