Urban Health Risk Mapping Urban Health Risk Mapping While estimating health outcomes at a neighborhood scale is important for promoting urban health, it has been a costly and time-consuming task. The Urban Health Risk Mapping project leverages crowdsourced data and machine learning technologies to predict the census tract-level health outcomes for ten major US cities, including Austin, Baltimore, Boston, Dallas, Washington, D.C., Houston, Los Angeles, New York City, San Antonio, and San Francisco. The machine-learning-enabled approach has an advantage over the traditional survey methods in terms of time and cost. Read more… Austin Baltimore Boston Dallas Houston Los Angeles New York City San Antonio San Francisco Washington D.C. Urban Health Risk Mapping The project consists of four parts: (1) database development, (2) modeling and analytics, (3) visualization and web development, and (4) community engagement and application. The first two parts are associated with the actual building, training, and testing of machine learning models. The targets are the various health outcomes, namely the prevalence of common non-communicable chronic diseases such as coronary heart disease, cancer, diabetes, poor mental health, obesity, and stroke. The actual health outcomes used in training and testing the models are accessed from the CDC’s 500 Cities Project. The features are created based on three data sources, namely the CDC’s Social Vulnerability Index (SVI) dataset, the EPA’s Smart Location Database (SLD), and the 311 service request datasets accessed from each municipality. Sixty features (i.e., predictor variables) are considered, which characterize the social environment, the physical environment, and the aspects and degrees of neighborhood disorder. A variety of machine learning algorithms are applied and compared, including Ridge Regression, Lasso Regression, Elastic Net, Support Vector Machine, Decision Tree, Random Forest, Extra Trees, and Gradient Boosting. To improve the model performance, the model hyperparameters are fine-tuned using 10-fold cross-validation. Different sets of features are also experimented with. It is shown that the tract-level prevalence for the common non-communicable chronic diseases can be reasonably well predicted based on the publicly available datasets. Furthermore, two major findings have been yielded from this study: (1) the sociodemographic and socioeconomic variables are the strongest predictors for tract-level health outcomes; (2) the historical records of 311 service requests can be a useful complementary data source because the information distilled from the 311 data often helps improve the models’ performance. The datasets and the predictive models are published online. Users can play with the models interactively by using the web tools we developed. The web tools can help the public and city officials evaluate future scenarios and understand how changes in the neighborhood conditions can lead to changes in the health outcomes. Data Sources The census tract-level health data are drawn from the 500 Cities Project dataset. (https://chronicdata.cdc.gov/browse?category=500+Cities) The built environment variables are calculated based on EPA’s Smart Location Database (SLD). (https://www.epa.gov/smartgrowth/smart-location-mapping#SLD) The socioeconomic and sociodemographic variables are extracted from CDC’s Social Vulnerability Index (SVI) dataset. (https://svi.cdc.gov/data-and-tools-download.html) The 311 data are accessed from the open data portal of each municipality: Austin: https://data.austintexas.gov/Utilities-and-City-Services/Austin-311-Public-Data/xwdj-i9he Baltimore: https://data.baltimorecity.gov/City-Services/311-Customer-Service-Requests/9agw-sxsr Boston: https://data.boston.gov/dataset/311-service-requests Dallas: https://www.dallasopendata.com/City-Services/311-Service-Requests-October-1-2016-to-September-3/shgm-yzbp https://www.dallasopendata.com/City-Services/311-Service-Requests-October-1-2018-to-Present-/m36q-vtbr Washington, D.C.: https://opendata.dc.gov/datasets/311-city-service-requests-in-2019 Houston: http://www.houstontx.gov/311/ Los Angeles: https://data.lacity.org/browse?q=311&sortBy=relevance&page=2 New York City: https://nycopendata.socrata.com/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9 San Antonio: https://data.sanantonio.gov/dataset/service-calls San Francisco: https://data.sfgov.org/City-Infrastructure/311-Cases/vw6y-z8j6/data Abbreviations and Descriptions of Variables Variable Abbreviation Data Source Outcome variable Arthritis among adults aged ≥ 18 years (%) ARTHRITIS CDC’s 500 Cities Project High blood pressure among adults aged ≥ 18 years (%) BPHIGH Cancer (excluding skin cancer) among adults aged ≥ 18 years (%) CANCER Current asthma prevalence among adults aged ≥ 18 years (%) CASTHMA Coronary heart disease among adults aged ≥ 18 years (%) CHD Chronic obstructive pulmonary disease among adults aged ≥ 18 years (%) COPD Diagnosed diabetes among adults aged ≥ 18 years (%) DIABETES High cholesterol among adults aged ≥ 18 years who have been screened in the past 5 years (%) HIGHCHOL Chronic kidney disease among adults aged ≥ 18 years (%) KIDNEY Mental health not good for ≥ 14 days among adults aged ≥ 18 years (%) MHLTH Physical health not good for ≥ 14 days among adults aged ≥ 18 years (%) PHLTH Stroke among adults aged ≥ 18 years (%) STROKE All teeth lost among adults aged ≥ 65 years (%) TEETHLOST Binge drinking prevalence among adults aged ≥ 18 years (%) BINGE Current smoking among adults aged ≥ 18 years (%) CSMOKING No leisure-time physical activity among adults aged ≥ 18 years LPA Obesity among adults aged ≥ 18 years OBESITY Sleeping less than 7 hours among adults aged ≥ 18 years SLEEP Note: The column names for the predicted health outcome values are made simply by prefixing a lowercase ‘p’ before the variable names shown above. For example, ‘ARTHRITIS’ becomes ‘pARTHRITIS’. Predictor variable Percentage of persons below poverty P_POV CDC’s SVI data Percentage of civilian (age 16+) unemployed estimate P_UNEMP Per capita income (US$) PCI Percentage of persons with no high school diploma (age 25+) P_NOHSDP Percentage of persons aged 65 and older P_AGE65P Percentage of persons aged 17 and younger P_AGE17M Percentage of civilian noninstitutionalized population with a disability P_DISABL Percentage of single parent households with children under 18 P_SNGPNT Percentage minority (all persons except white, non-Hispanic) P_MINRTY Percentage of persons (age 5+) who speak English “less than well” P_LIMENG Percentage of housing in structures with 10 or more units P_MUNIT Percentage of mobile homes P_MOBILE Percentage of occupied housing units with more people than rooms P_CROWD Percentage of households with no vehicle available P_NOVEH Percentage of persons in institutionalized group quarters P_GROUPQ Percentage uninsured in the total civilian noninstitutionalized population P_UNINSUR Percent of population that is working aged P_WRKAGE EPA’s Smart Location Database Percent of one-car households P_AO1 Percent of two-plus-car households P_AO2P Percentage of low-wage workers (earning $1250/month or less) among total workers (home location) P_LOWWAGEr Percentage of low-wage workers (earning $1250/month or less) among total workers (work location) P_LOWWAGEe Gross residential density (HU/acre) on unprotected land D_HH Gross population density (people/acre) on unprotected land D_POP Gross employment density (jobs/acre) on unprotected land D_EMP Gross activity density (employment + HUs) on unprotected land D_HUEMP Gross retail (5-tier) employment density (jobs/acre) on unprotected land D_EMP_RET Gross office (5-tier) employment density (jobs/acre) on unprotected land D_EMP_OFF Gross industrial (5-tier) employment density (jobs/acre) on unprotected land D_EMP_IND Gross service (5-tier) employment density (jobs/acre) on unprotected land D_EMP_SVC Gross entertainment (5-tier) employment density (jobs/acre) on unprotected land D_EMP_ENT Jobs per household JOBSPERHH 5-tier employment entropy (denominator set to observed employment types in the census tract) EMPMIX Employment and household entropy EMPHHMIX Employment and household entropy (based on vehicle trip production and trip attractions including all 5 employment categories) TRIPMIX Trip productions and trip attractions equilibrium index TRIPEQ Household workers per job, by census tract WRKSPERJOB Household workers per job equilibrium index HHWRKJOBEQ Total road network density D_RD Network density in terms of facility miles of auto-oriented links per square mile D_RD_AO Network density in terms of facility miles of multi-modal links per square mile D_RD_MM Network density in terms of facility miles of pedestrian-oriented links per square mile D_RD_PO Street intersection density (auto-oriented intersections eliminated) D_X_EXCLAO Intersection density in terms of auto-oriented intersections per square mile D_X_AO Intersection density in terms of multi-modal intersections having three legs per square mile D_X_MM3 Intersection density in terms of multi-modal intersections having four or more legs per square mile D_X_MM4 Intersection density in terms of pedestrian-oriented intersections having three legs per square mile D_X_PO3 Intersection density in terms of pedestrian-oriented intersections having four or more legs per square mile D_X_PO4 Proportion of census tract employment within ¼ mile of fixed-guideway transit stop P_EMP025 Proportion of census tract employment within ½ mile of fixed-guideway transit stop P_EMP050 Aggregate frequency of transit service per square mile D_TRANSIT
