Vulnerability of Large City and Its Implication in Urban Planning: A Perspective of Intra-urban Structure

Vulnerability is a new field and analytical tool in the study of urban safety. Analysis and assessment of vulnerability provide a new basis for urban planning. This study constructed a quantitative index system for assessing vulnerability, based on the city′s sensitivity and emergency response capacity. City size, density, and spatial form influence a city′s sensitivity to crises and risks, to which vulnerability is positively related. Levels of socio-economic development, infrastructures, and emergency management contribute to a city′s emergency response capacity, with which vulnerability is inversely associated. Vulnerability of 19 large Chinese cities was assessed. Harbin and Shenzhen demonstrated the highest and lowest vulnerability among 19 cities, while Beijing, Shanghai and Guangzhou ranked the 5th, the 9th and the 12th. Spatially, northern cities tended to be more vulnerable than southern cities. And the differences in vulnerability among cities were explored based on cities′ physical geography conditions, level of socioeconomic development, infrastructures, regional status, history of disaster, history of urban planning and development, government policies, etc.

Spatial Patterns of Car Sales and Their Socio-economic Attributes in China

Using data from the Economic Advisory Center of the State Information Center(SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions of car sales of different kinds of automakers(foreign automakers, Sino-foreign joint automakers, and Chinese automakers), and then identified spatial clusters using the local Moran's indexes. Location quotient analysis was applied to examine the relative advantage of each type of automaker in the local markets. To explain the variations of car sales across cities, we collected several socioeconomic variables and conducted regression analyses. Further, factor analysis was used to extract independent variables to avoid the problem of multicollinearity. By incorporating a spatial lag or spatial error in the models, we calibrated our spatial regression models to address the spatial dependence problem. The analytical results show that car sales varied significantly across cities in China, and most of the cities with higher car sales were the developed cities. Different automakers exhibit diverse spatial patterns in terms of car sales volume, spatial clusters, and location quotients. The scale and incomes factor were extracted and verified as the two most significant and positive factors that shape the spatial distributions of car sales, and together with the spatial effect, explained most of the variations of car sales across cities.

Modeling Urban Hydrology: A Comparison of New Urbanist and Traditional Neighborhood Design Surface Runoff

Urban development generally leads to an increase in impervious cover resulting in a greater volume of surface runoff following storm activity. However, the type of urban development in place strongly controls the degree of impervious cover generated. Traditional neighborhood designs focus on a medium-to-low urban density spread over larger areas, while new urbanist neighborhood designs incorporate more diversity by increasing urban density across smaller areas. The purpose of this study is to model and compare the potential surface runoff for two urban neighborhoods in Austin, Texas-Circle C Ranch, a traditional neighborhood design, and Mueller, a new urbanist development for a 10-year 24-hour storm scenario. Potential surface runoff was calculated by layering various geospatial datasets representing the physical characteristics of both study sites within the Watershed Modeling System (WMS) to configure the HEC-HMS runoff model. Results initially imply that the higher density new urbanist neighborhood significantly increases total and peak storm runoff compared to the traditional neighborhood. However, a greater number of residential units are available at Mueller over the same area as Circle C Ranch. When taking this into account the increased potential surface runoff is negated at the new urbanist site. Although new urbanist neighborhoods will usually contain more residential units than traditional developments when compared at the same scale, the higher urban density associated with these neighborhoods demand the development of more effective stormwater retention systems to cope with a potential increase in surface runoff.

Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data.However,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census data.Here we tested two methods using remotely sensed,built-area land cover data to disaggregate population data.These included simple,areal weighting and more complex statistical models with other ancillary information.Outcomes were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental characteristics.We found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built areas.For these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive capacity.We discuss these results and implications for future population modeling approaches.

Exploring nationally and regionally defined models for large area population mapping

Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.

Modelling changing population distributions:an example of the Kenyan Coast,1979–2009

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates.Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes.Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit.Here we make use of recently released multi-temporal high-resolution global settlement layers,historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast.We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach.Strategies used to fill data gaps may vary according to the local context and the objective of the study.This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.

Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.