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US 2010/0305913 A1Published application
Method of modeling the socio-spatial dynamics of extreme urban heat events
OwnerDaniel P. Johnson, Jeffrey S. Wilsoninventors
Life of this patent
Estimating the term…
Abstract
A method of coupling surface urban heat island measures with socio-economic indicators of vulnerability to create improved spatially specific models to assist public health professionals in predicting extreme heat events mortality in urban environments. The method includes utilizing landsat TM imagery for the measuring of the urban heat island intensity levels and a spatial analysis of the variables in question.
Claims26
A method for creating a risk model for determining the spatial specificity of the most at-risk subpopulations from an extreme heat event within an urban setting, comprising:
- (a) generating a data set at the block group level from census data, wherein a block group represents an area-standardized partition on a grid map of an urban setting and wherein the data set further comprises groupings of population apportioned according to a plurality of predetermined vulnerability characteristics;
- (b) generating estimates of population density data at the block group level;
- (c) generating heat-related decedent data;
- (d) generating urban heat island intensity level data;
- (e) generating lists of the significant socio-economic variables of vulnerability and of the significant urban heat island intensity levels from the data set at the block group level and the estimated population density at the block group level and the heat-related decedent data and the urban heat island intensity levels;
- (f) generating a predictive vulnerability assessment model using the significant social economic variables of vulnerability and the significant urban heat island intensity levels and the heat-related decedent data;
- and
- (g) allocating city resources according to the predictive vulnerability assessment model in response to an extreme heat event event.
5 more elements
A method for determining the significance of a social economic variable of vulnerability within the context of a urban heat island effect, with respect to urban extreme heat event mortalities comprising:
- (a) calculating the centroid of each plurality of block group, wherein each respective block group represents an equal portion of the area on a grid map of an urban setting;
- (b) assigning measures of socio-economic vulnerability, decedents, and stratified temperature to the centroid;
- (c) calculating a weighted mean center of extreme heat event mortalities;
- (d) calculating weighted mean centers of socio-economic variables of vulnerability using the respective centroids as points;
- (e) calculating a weighted mean center of stratified temperatures using the respective centroids as points;
- (f) calculating an EHE standard deviation ellipse of the extreme heat event mortalities, using the weighted mean center as the origin;
- (g) calculating an eccentricity value for the EHE standard deviation ellipse of the extreme heat event mortalities;
- (h) calculating a respective standard deviation ellipse for each socio-economic variable;
- (i) calculating a respective standard deviation ellipse for each stratified temperature value using the corresponding weighted mean center;
- (j) using T-test and F-test techniques to determine spatial similarity between the respective EHE standard deviation ellipses and the respective standard deviation ellipses for each socio-economic variable and for each stratified temperature value;
- (k) calculating an extreme heat event mortality concentration within each standard deviation ellipse for each socio-economic variable and for each respective stratified temperature value shown to be spatially similar as the number of extreme heat event mortalities coincident within the standard deviation ellipse divided by the area of the standard deviation ellipse;
- (l) calculating the eccentricity for each respective socio-economic variable and for each respective stratified temperature value identified to be spatially similar;
- (m) marking each identified spatially similar socio-economic variable and stratified temperature value significant where the extreme heat event mortality concentration is above a predetermined threshold and the eccentricity value is within a predetermined range of the eccentricity of the extreme heat event mortality;
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- (n) providing an output of the significant socio-economic variables and stratified temperature values.
12 more elements
A method for creating a predictive model for the spatial concentrations of extreme heat event mortality within an urban setting utilizing socio-economic indicators of vulnerability and stratified urban heat island effects, comprising:
- (a) allocating census data to a plurality of block groups, wherein each respective block group represents an equal area on a grid map of an urban setting and wherein allocation of census data further comprises differentiation of population according to a plurality of predetermined vulnerability characteristics;
- (b) calculating estimates of population density at the block group level;
- (c) counting the number of heat-related decedents;
- (d) measuring urban heat island intensity levels;
- (e) stratifying the measured urban heat island intensity values;
- (f) designating significant socio-economic variables of vulnerability and the significant urban heat island intensity levels from the allocated and differentiated census data set at the block group level and the estimated population densities and the number of heat-related decedents and the urban heat island intensity levels;
- (g) creating a predictive model for the spatial concentrations of extreme heat event mortality in an urban environment using the designated significant social economic variables of vulnerability and the significant urban heat island intensity levels and the heat-related decedent data;
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- (h) distributing city resources according to the predictive model for the spatial concentrations of extreme heat event mortality in an urban environment in response to an extreme heat event.
6 more elements
Dependencies are inferred from the claim text.