For current version, see: https://data.sandiegocounty.gov/Live-Well-San-Diego/Live-Well-San-Diego-Database/wsyp-5xpf/about_data

This is the official Live Well San Diego database with the Top 10 Indicators and Expanded Indicators. Baseline data begins in 2009 where data available and continues through current day. Data is collected on an annual basis.

For definitions and sourcing, please use the Live Well San Diego Data Dictionary: https://data.sandiegocounty.gov/Live-Well-San-Diego/Live-Well-San-Diego-Data-Dictionary/remr-mk73

• indicates statistically unstable estimate.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services Division, Community Health Statistics Unit.

This is the official Live Well San Diego database with the Top 10 Indicators and Expanded Indicators. Baseline data begin in 2012 where data are available and continue through current day. Data are collected on an annual basis.

For definitions and sourcing, please use the Live Well San Diego Data Dictionary: https://data.sandiegocounty.gov/Live-Well-San-Diego/Live-Well-San-Diego-Data-Dictionary/37vr-nftn/about_data

Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services Division, Community Health Statistics Unit.

Shrublands have seen large changes over time due to factors such as fire and drought. As the climate continues to change, vegetation monitoring at the county scale is essential to identify large-scale changes and to develop sampling designs for field-based vegetation studies. This dataset contains two raster files that each depict the height of vegetation. The first layer is restricted to actively growing vegetation and the second is restricted to dormant/dead vegetation. Both layers cover open space areas in San Diego County, California. Height calculations were derived from Lidar data collected in 2014 and 2015 for the western two-thirds of San Diego County. Lidar point clouds were pre-classified into ground and non-ground. Rasters for the Digital Elevation Model (DEM) and Digital Surface Model (DSM) were calculated using ArcGIS software using ground classified points and last returns for the natural surface (DEM) and non-ground first returns for the surface model (DSM). The spatial resolution for both layers is 1 meter and aligns with 2014 National Agriculture Imagery Program (NAIP) imagery. Object height was calculated by subtracting the DEM from the DSM in meters. To remove structures or non-natural objects from the imagery, layers were clipped to open space areas using the National Land Cover Database, building footprints, roads, and railways. This ensures that objects above the natural surface are vegetation, even when Normalized Difference Vegetation Index (NDVI) numbers are very low. NDVI measures the amount of photosynthetically active vegetation in the raster cell. Healthy vegetation reflects high levels of near-infrared and low levels of red electromagnetic radiation. NDVI ranges from -1 to 1 with low values indicating little or no presence of healthy vegetation and higher values indicating the presence of healthy vegetation. The NDVI was calculated from the 2014 NAIP imagery and a cutoff of 0.1 was used to separate photosynthetically active vegetation from non-vegetated or dormant/dead vegetation areas. The imagery was collected during 2014, an exceptional drought year. It is not possible to separate extremely water-stressed plants from truly dead plants using only NDVI. The natural surface was verified using established National Geodetic Survey (NGS) benchmarks and exceeded 98 percent accuracy. Vegetation structure was validated using visual assessments of high-resolution aerial imagery to verify the vegetation form and greenness. Vegetation form and health (NDVI) had an accuracy of 82 percent.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

The current dataset is a subset of a large data collection based on a purpose-built survey conducted in seven middle-income countries in the Global South: Chile, Colombia, India, Kenya, Nigeria, Tanzania, South Africa and Vietnam. The purpose of the collected variables in the present dataset aims to understanding public preferences as a critical way to any effort to reduce greenhouse gas emissions. There are many studies of public preferences regarding climate change in the Global North. However, survey work in low and middle-income countries is limited. Survey work facilitating cross-country comparisons not using the major omnibus surveys is relatively rare.
We designed the Environment for Development (EfD) Seven-country Global South Climate Survey (the EfD Survey) which collected information on respondents’ knowledge about climate change, the information sources that respondents rely on, and opinions on climate policy. The EfD survey contains a battery of well-known climate knowledge questions and questions concerning the attention to and degree of trust in various sources for climate information. Respondents faced several ranking tasks using a best-worst elicitation format. This approach offers greater robustness to cultural differences in how questions are answered than the Likert-scale questions commonly asked in omnibus surveys. We examine: (a) priorities for spending in thirteen policy areas including climate and COVID-19, (b) how respiratory diseases due to air pollution rank relative to six other health problems, (c) agreement with ten statements characterizing various aspects of climate policies, and (d) prioritization of uses for carbon tax revenue. The company YouGov collected data for the EfD Survey in 2023 from 8400 respondents, 1200 in each country. It supplements an earlier survey wave (administered a year earlier) that focused on COVID-19. Respondents were drawn from YouGov’s online panels. During the COVID-19 pandemic almost all surveys were conducted online. This has advantages and disadvantages. Online survey administration reduces costs and data collection times and allows for experimental designs assigning different survey stimuli. With substantial incentive payments, high response rates within the sampling frame are achievable and such incentivized respondents are hopefully motivated to carefully answer the questions posed. The main disadvantage is that the sampling frame is comprised of the internet-enabled portion of the population in each country (e.g., with computers, mobile phones, and tablets). This sample systematically underrepresents those with lower incomes and living in rural areas. This large segment of the population is, however, of considerable interest in its own right due to its exposure to online media and outsized influence on public opinion. The data includes respondents’ preferences for climate change mitigation policies and competing policy issues like health. The data also includes questions such as how respondents think revenues from carbon taxes should be used. The outcome provide important information for policymakers to understand, evaluate, and shape national climate policies. It is worth noting that the data from Tanzania is only present in Wave 1 and that the data from Chile is only present in Wave 2.

The datasets contain reimbursement rates paid to participating Program of All-Inclusive Care for the Elderly (PACE) organizations for calendar years 2015-2025. To be eligible for the PACE program, a person must be 55 years of age or older and reside in one of the following PACE service areas: Alameda, Contra Costa, Fresno, Humboldt, Los Angeles, Orange, Riverside, Sacramento, San Bernardino, San Diego, San Francisco, San Joaquin, Santa Clara, Stanislaus.