Relative concentration of the estimated number of people in the Southern California region that live in a household defined as "low income." There are multiple ways to define low income. These data apply the most common standard: low income population consists of all members of households that collectively have income less than twice the federal poverty threshold that applies to their household type. Household type refers to the household's resident composition: the number of independent adults plus dependents that can be of any age, from children to elderly. For example, a household with four people ' one working adult parent and three dependent children ' has a different poverty threshold than a household comprised of four unrelated independent adults.

Due to high estimate uncertainty for many block group estimates of the number of people living in low income households, some records cannot be reliably assigned a class and class code comparable to those assigned to race/ethnicity data from the decennial Census.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region. See the "Data Units" description below for how these relative concentrations are broken into categories in this "low income" metric.

Source

This is the dataset used in this book: https://github.com/ageron/handson-ml/tree/master/datasets/housing to illustrate a sample end-to-end ML project workflow (pipeline). This is a great book - I highly recommend!

The data is based on California Census in 1990.

About the Data (from the book):

"This dataset is a modified version of the California Housing dataset available from Luís Torgo's page (University of Porto). Luís Torgo obtained it from the StatLib repository (which is closed now). The dataset may also be downloaded from StatLib mirrors.

The following is the description from the book author:

This dataset appeared in a 1997 paper titled Sparse Spatial Autoregressions by Pace, R. Kelley and Ronald Barry, published in the Statistics and Probability Letters journal. They built it using the 1990 California census data. It contains one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).

The dataset in this directory is almost identical to the original, with two differences: 207 values were randomly removed from the total_bedrooms column, so we can discuss what to do with missing data. An additional categorical attribute called ocean_proximity was added, indicating (very roughly) whether each block group is near the ocean, near the Bay area, inland or on an island. This allows discussing what to do with categorical data. Note that the block groups are called "districts" in the Jupyter notebooks, simply because in some contexts the name "block group" was confusing."

About the Data (From Luís Torgo page):

http://www.dcc.fc.up.pt/%7Eltorgo/Regression/cal_housing.html

This is a dataset obtained from the StatLib repository. Here is the included description:

"We collected information on the variables using all the block groups in California from the 1990 Cens us. In this sample a block group on average includes 1425.5 individuals living in a geographically co mpact area. Naturally, the geographical area included varies inversely with the population density. W e computed distances among the centroids of each block group as measured in latitude and longitude. W e excluded all the block groups reporting zero entries for the independent and dependent variables. T he final data contained 20,640 observations on 9 variables. The dependent variable is ln(median house value)."

End-to-End ML Project Steps (Chapter 2 of the book)

1. Look at the big picture
2. Get the data
3. Discover and visualize the data to gain insights
4. Prepare the data for Machine Learning algorithms
5. Select a model and train it
6. Fine-tune your model
7. Present your solution
8. Launch, monitor, and maintain your system

The 10-Step Machine Learning Project Workflow (My Version)

1. Define business object
2. Make sense of the data from a high level
   • data types (number, text, object, etc.)
   • continuous/discrete
   • basic stats (min, max, std, median, etc.) using boxplot
   • frequency via histogram
   • scales and distributions of different features
3. Create the traning and test sets using proper sampling methods, e.g., random vs. stratified
4. Correlation analysis (pair-wise and attribute combinations)
5. Data cleaning (missing data, outliers, data errors)
6. Data transformation via pipelines (categorical text to number using one hot encoding, feature scaling via normalization/standardization, feature combinations)
7. Train and cross validate different models and select the most promising one (Linear Regression, Decision Tree, and Random Forest were tried in this tutorial)
8. Fine tune the model using trying different combinations of hyperparameters
9. Evaluate the model with best estimators in the test set
10. Launch, monitor, and refresh the model and system

Relative concentration of the Southern California region's Hispanic/Latino population. The variable HISPANIC records all individuals who select Hispanic or Latino in response to the Census questionnaire, regardless of their response to the racial identity question.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as American Indian / Alaska Native alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as American Indian / Alaska native alone. Example: if 5.2% of people in a block group identify as HISPANIC, the block group has twice the proportion of HISPANIC individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HISPANIC individuals are highly concentrated locally.

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables.

Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

"The Social Security Administration (SSA) suggested to USC to survey members of the public around these topics: What do people know about Social Security? How do people learn about Social Security and how do they want to learn about Social Security? How do adults use financial products as they age? How do adults make their financial decisions and where do they turn for advice? What are adults' main sources of financial stress? The results of the survey are available at the USC website below after logging in and being granted access by USC."

Updated 10/6/2022: In the Time/Distance analysis process, points that were found to have been included initially, but with no significant or year-round population were removed. The layer of removed points is also available for viewing. MCNA - Removed Population PointsThe Network Adequacy Standards Representative Population Points feature layer contains 97,694 points spread across California that were created from USPS postal delivery route data and US Census data. Each population point also contains the variables for Time and Distance Standards for the County that the point is within. These standards differ by County due to the County "type" which is based on the population density of the county. There are 5 county categories within California: Rural (<50 people/sq mile), Small (51-200 people/sq mile), Medium (201-599 people/sq mile), and Dense (>600 people/sq mile). The Time and Distance data is divided out by Provider Type, Adult and Pediatric separately, so that the Time or Distance analysis can be performed with greater detail. HospitalsOB/GYN SpecialtyAdult Cardiology/Interventional CardiologyAdult DermatologyAdult EndocrinologyAdult ENT/OtolaryngologyAdult GastroenterologyAdult General SurgeryAdult HematologyAdult HIV/AIDS/Infectious DiseaseAdult Mental Health Outpatient ServicesAdult NephrologyAdult NeurologyAdult OncologyAdult OphthalmologyAdult Orthopedic SurgeryAdult PCPAdult Physical Medicine and RehabilitationAdult PsychiatryAdult PulmonologyPediatric Cardiology/Interventional CardiologyPediatric DermatologyPediatric EndocrinologyPediatric ENT/OtolaryngologyPediatric GastroenterologyPediatric General SurgeryPediatric HematologyPediatric HIV/AIDS/Infectious DiseasePediatric Mental Health Outpatient ServicesPediatric NephrologyPediatric NeurologyPediatric OncologyPediatric OphthalmologyPediatric Orthopedic SurgeryPediatric PCPPediatric Physical Medicine and RehabilitationPediatric PsychiatryPediatric Pulmonology

Relative concentration of the Southern California region's Asian American population. The variable ASIANALN records all individuals who select Asian as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with the Asian race alone.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as ASIANALN alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as ASIANALN alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of ASIANALN individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then ASIANALN individuals are highly concentrated locally.

The 2023 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

The Relative concentration of the Southern California region's population that identifies as "Multiracial", EXCEPT those with part-American Indian identity, in response to the Census questionnaire. "Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identifies as Multiiracial to the proportion of all people that live within the 13,312 census block groups in the Southern California RRK region. People with part-American Indian identity are not included here but are included in the American Indian or Alaska Native Race Alone and Multirace Population, described above.

This feature service is derived from the Esri "United States Zip Code Boundaries" layer, queried to only CA data.For the original data see: https://esri.maps.arcgis.com/home/item.html?id=5f31109b46d541da86119bd4cf213848Published by the California Department of Technology Geographic Information Services Team.The GIS Team can be reached at ODSdataservices@state.ca.gov.U.S. ZIP Code Boundaries represents five-digit ZIP Code areas used by the U.S. Postal Service to deliver mail more effectively. The first digit of a five-digit ZIP Code divides the United States into 10 large groups of states (or equivalent areas) numbered from 0 in the Northeast to 9 in the far West. Within these areas, each state is divided into an average of 10 smaller geographical areas, identified by the second and third digits. These digits, in conjunction with the first digit, represent a Sectional Center Facility (SCF) or a mail processing facility area. The fourth and fifth digits identify a post office, station, branch or local delivery area.As of the time this layer was published, in January 2025, Esri's boundaries are sourced from TomTom (June 2024) and the 2023 population estimates are from Esri Demographics. Esri updates its layer annually and those changes will immediately be reflected in this layer. Note that, because this layer passes through Esri's data, if you want to know the true date of the underlying data, click through to Esri's original source data and look at their metadata for more information on updates.Cautions about using Zip Code boundary dataZip code boundaries have three characteristics you should be aware of before using them:Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.As of early 2025, USPS appears to be in the process of releasing boundaries, which will at least provide an authoritative source, but because of the other factors above, we do not recommend these boundaries for many use cases. If you are using these for anything other than mailing purposes, we recommend reconsideration. We provide the boundaries as a convenience, knowing people are looking for them, in order to ensure that up-to-date boundaries are available.

Ernest-etal-Genotypes_PLosOne2014README file: Ernest et al. California puma (mountain lion) genetics published in PLOS ONE 2014. Title: Fractured genetic connectivity threatens a southern California puma (Puma concolor) population. Authors: Holly B. Ernest, T. Winston Vickers, Scott A. Morrison, Michael R. Buchalski, Walter M. Boyce. Corresponding author: Holly B. Ernest hbernest-at-ucdavis-dot-edu and hernest-at-uwyo-dot-edu. Data file (tab-delim. text): Ernest-etal-Genotypes_PLosOne2014_FracturedConnecPumasCalif_14Aug2014.txt. File contents: Sample ID #; Population identifier; 3-digit nuclear microsatellite genotypic data (diploid, one allele per column, two columns per locus) for 46 loci (42 for the focal populations of this paper). Locus identifier is listed in the first of the two columns for data of each locus. Data in one row per individual sample.

Relative concentration of the Southern California region's Black/African American population. The variable BLACKALN records all individuals who select black or African American as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with black race alone.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as Black/African American alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as Black/African American alone. Example: if 5.2% of people in a block group identify as BLACKALN, the block group has twice the proportion of BLACKALN individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then BLACKALN individuals are highly concentrated locally.

Annual biomass data (2001-2021) for southern California: above- and below-ground, standing dead, and litter

https://doi.org/10.5061/dryad.qz612jmjt

Description of the data and file structure

Data description:

Annual spatial estimates of above ground live, standing dead, litter, and below ground biomass (g/m2) for 2001-2023 for southern California.

These raster layers were created by modeling field plot biomass to covariates, including precipitation, remotely sensed NDVI, and geophysical (slope, aspect, elevation) data.

For a more complete description, visit https://doi.org/10.5061/dryad.qz612jmjt

The biomass raster layers are packaged in zip files for each year using the following naming structure:

WWETAC_UCD_SoCal_Biomass_XXXX.zip

Where XXXX is the year of the biomass estimates. Within each zip file are the following files:

WWETAC_UCD_

Where

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Race is a social and historical construct, and the racial categories counted by the census change over time so the process of constructing stable racial categories for these 50 years out of census data required complex and imperfect decisions. Here we have used historical research on early 20th century southern California to construct historic racial categories from the IPUMS full count data, which allows us to track groups that were not formally classified as racial groups in some census decades like Mexican, but which were important racial categories in southern California. Detailed explanation of how we constructed these categories and the rationale we used for the decisions we made can be found here. Layers are symbolized to show the percentage of each of the following groups from 1900-1940:AmericanIndian Not-Hispanic, AmericanIndian Hispanic, Black non-Hispanic, Black-Hispanic, Chinese, Korean, Filipino and Japanese, Mexican, Hispanic Not-Mexican, white non-Hispanic. The IPUMS Census data is messy and includes some errors and undercounts, making it hard to map some smaller populations, like Asian Indians (in census called Hindu in 1920) and creating a possible undercount of Native American populations. The race data mapped here also includes categories that may not have been socially meaningful at the time like Black-Hispanic, which generally would represent people from Mexico who the census enumerator classified as Black because of their dark skin, but who were likely simply part of Mexican communities at the time. We have included maps of the Hispanic not-Mexican category which shows very small numbers of non-Mexican Hispanic population, and American Indian Hispanic, which often captures people who would have been listed as Indian in the census, probably because of skin color, but had ancestry from Mexico (or another Hispanic country). This category may include some indigenous Californians who married into or assimilated into Mexican American communities in the early 20th century. If you are interested in mapping some of the other racial or ethnic groups in the early 20th century, you can explore and map the full range of variables we have created in the People's History of the IE IE_ED1900-1940 Race Hispanic Marriage and Age Feature layer.Suggested Citation: Tilton, Jennifer. People's History Race Ethnicity Dot Density Map 1900-1940. A People's History of the Inland Empire Census Project 1900-1940 using IPUMS Ancestry Full Count Data. Program in Race and Ethnic Studies University of Redlands, Center for Spatial Studies University of Redlands, UCR Public History. 2023. 2025Feature Layer CitationTilton, Jennifer, Tessa VanRy & Lisa Benvenuti. Race and Demographic Data 1900-1940. A People's History of the Inland Empire Census Project 1900-1940 using IPUMS Ancestry Full Count Data. Program in Race and Ethnic Studies University of Redlands, Center for Spatial Studies University of Redlands, UCR Public History. 2023. Additional contributing authors: Mackenzie Nelson, Will Blach & Andy Garcia Funding provided by: People’s History of the IE: Storyscapes of Race, Place, and Queer Space in Southern California with funding from NEH-SSRC Grant 2022-2023 & California State Parks grant to Relevancy & History. Source for Census Data 1900- 1940 Ruggles, Steven, Catherine A. Fitch, Ronald Goeken, J. David Hacker, Matt A. Nelson, Evan Roberts, Megan Schouweiler, and Matthew Sobek. IPUMS Ancestry Full Count Data: Version 3.0 [dataset]. Minneapolis, MN: IPUMS, 2021. Primary Sources for Enumeration District Linework 1900-1940 Steve Morse provided the full list of transcribed EDs for all 5 decades "United States Enumeration District Maps for the Twelfth through the Sixteenth US Censuses, 1900-1940." Images. FamilySearch. https://FamilySearch.org: 9 February 2023. Citing NARA microfilm publication A3378. Washington, D.C.: National Archives and Records Administration, 2003. BLM PLSS Map Additional Historical Sources consulted include: San Bernardino City Annexation GIS Map Redlands City Charter Proposed with Ward boundaries (Not passed) 1902. Courtesy of Redlands City Clerk. Redlands Election Code Precincts 1908, City Ordinances of the City of Redlands, p. 19-22. Courtesy of Redlands City Clerk Riverside City Charter 1907 (for 1910 linework) courtesy of Riverside City Clerk. 1900-1940 Raw Census files for specific EDs, to confirm boundaries when needed, accessed through Family Search. If you have additional questions or comments, please contact jennifer_tilton@redlands.edu.

Contact us at insights@fantastic.app for more details and questions. Visit https://fantastic.app to learn more about how our dataset is created.

What problem is Fantastic solving?

Personalization is no longer a nice to have for today's consumers, it is now a necessity. According to a recent study by Mckinsey "Seventy-one percent of consumers expect companies to deliver personalized interactions. And seventy-six percent get frustrated when this doesn’t happen!"

Although the stakes for personalization have never been higher, it is increasingly difficult for businesses to deliver personalized interactions while respecting consumer data privacy. As explained in a recent Zendesk CX Trends Report "62% of consumers want more personalized experiences, but only 21% strongly agree that businesses are doing enough to protect their data." Investing in architecture to collect, clean, categorize, and preserve consumer interest data is a costly process for many businesses and often creates friction for consumers expecting instant personalization.

Fantastic closes this personalization gap for businesses by collecting consumer interest data from a panel of users who are rewarded for sharing their favorite products, content, and services. This data is cleaned and categorized by age, gender, city, and country to make it easy for businesses to uncover patterns. This data is granted full consent for commercial use and anonymized for user privacy, ready for instant use in delivering personalized interactions.

How is Fantastic unique?

Since 2017, Fantastic has been building a platform that makes it easy and rewarding for consumers to share their favorite products and content. This leads to an authentic and detailed dataset shaped directly by the voice of the consumer. Our dataset is dynamic and continuously growing, enabling businesses to stay up to date with shifiting consumer trends.

Use cases

Improve Ad Conversions – Create more effective advertisements by understanding shifting consumer preferences. Use insights from various audiences' favorite content, products, and media to reach your intended audience with marketing that resonates.

Instant Personalization – Overcome the cold start problem for recommender systems by enriching your users' profiles with interest data from our dataset.

Create Products & Content People Love – Leverage consumer interests from outside your ecosystem to gain insights on shifting market trends, gather competitive intelligence, and adopt highly-requested product features.

Product details

Consumer interests represented in fantastic_insights_dataset table

Free sample dataset consists of 1000 user reviews

Full dataset consists of 30,000+ user reviews from ~1000 audience panel members

Audience panel members are located in the United States and represent all major U.S. regions and demographics. The most represented demographics in the dataset are 18-35 males and 18-35 females in Southern California.

Each dataset row is a positive review of a product/service/content from users on our platform. Each row includes the following fields:

gender (User gender: Male | Female | Non-Binary) age_range (User age range: 13-17 | 18-24 | 25-34 | 35-44 | 45-54 | 55-64 |65+ ) city_name (User city) state_name (User state) country_name (User country) user_count (Number of users that endorse the review, for multiple endorsers) subject (Product, service, or content endorsed) description (Description of product, service, or content) link (Link to product, service, or content) image_link (Link to image of product, service, or content) tag_1 (User provided category for product, service or content) tag_2 (User provided category for product, service or content) tag_3 (User provided category for product, service or content) tag_4 (User provided category for product, service or content) tag_5 (User provided category for product, service or content) tag_6 (User provided category for product, service or content) tag_7 (User provided category for product, service or content) tag_8 (User provided category for product, service or content) tag_9 (User provided category for product, service or content) tag_10 (User provided category for product, service or content)

CalEnviroScreen is a mapping tool that helps identify California communities that are most affected by many sources of pollution, and where people are often especially vulnerable to pollution’s effects.
CalEnviroScreen uses environmental, health, and socioeconomic information to produce scores for every census tract in the state.
The scores are mapped so that different communities can be compared. An area with a high score is one that experiences a much higher pollution burden than areas with low scores.
CalEnviroScreen ranks communities based on data that are available from state and federal government sources.

Relative concentration of the Southern California region's Black/African American population. The variable HSPBIPOC is equivalent to all individuals who select a combination of racial and ethnic identity in response to the Census questionnaire EXCEPT those who select "not Hispanic" for the ethnic identity question, and "white race alone" for the racial identity question. This is the most encompassing possible definition of racial and ethnic identities that may be associated with historic underservice by agencies, or be more likely to express environmental justice concerns (as compared to predominantly non-Hispanic white communities). Until 2021, federal agency guidance for considering environmental justice impacts of proposed actions focused on how the actions affected "racial or ethnic minorities." "Racial minority" is an increasingly meaningless concept in the USA, and particularly so in California, where only about 3/8 of the state's population identifies as non-Hispanic and white race alone - a clear majority of Californians identify as Hispanic and/or not white. Because many federal and state map screening tools continue to rely on "minority population" as an indicator for flagging potentially vulnerable / disadvantaged/ underserved populations, our analysis includes the variable HSPBIPOC which is effectively "all minority" population according to the now outdated federal environmental justice direction. A more meaningful analysis for the potential impact of forest management actions on specific populations considers racial or ethnic populations individually: e.g., all people identifying as Hispanic regardless of race; all people identifying as American Indian, regardless of Hispanic ethnicity; etc.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as HSPBIPOC alone to the proportion of all people that live within the 13,312 block groups in the Southern California RRK region that identify as HSPBIPOC alone. Example: if 5.2% of people in a block group identify as HSPBIPOC, the block group has twice the proportion of HSPBIPOC individuals compared to the Southern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then HSPBIPOC individuals are highly concentrated locally.

Severe burns are one of the most complex forms of traumatic injury. People with burn injuries often require long-term rehabilitation. Survivors of a burn injury often have a wide range of physical and psychosocial problems that can affect their quality of life. The Burn Model System (BMS) program began in 1994, with funding from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), in the Administration of Community Living and the U.S. Department of Education. The BMS program seeks to improve, through research, care and outcomes for people with burn injuries. Its research programs are housed in clinical burn centers that provide a coordinated and multidisciplinary system of rehabilitation care, including emergency medical, acute medical, post-acute, and long-term follow-up services. In addition, and with funding from NIDILRR, each BMS center conducts research and contributes follow-up data to the BMS National Data and Statistical Center (BMS NDSC). The four BMS centers are: Boston-Harvard Burn Injury Model System (BH-BIMS) in Boston, Massachusetts North Texas Burn Rehabilitation Model System (NTBRMS) in Dallas, Texas Northwest Regional Burn Model System (NWRBMS) in Seattle, Washington; andSouthern California Burn Model System (SCBMS) in Los Angeles, CaliforniaPast centers include the University of Texas Medical Branch Burn Injury Rehabilitation Model System in Galveston, Texas, the Johns Hopkins University Burn Model System in Baltimore, Maryland, the University of Colorado Denver National Data and Statistical Center, and the University of Colorado Denver Burn Model System Center.The BMS NDSC supports the research teams in the clinical burn centers. It also manages data collected by the BMS centers on more than 7,000 people who have received medical care for burn injuries. The data include a wide range of information—including pre-injury; injury; acute care; rehabilitation; recovery; and outcomes at 6, 12, 24 months, and every five years after the burn injury. To be included in the database, the burn injuries of participants must meet several criteria (as of 2015): ·More than 10% total body surface area (TBSA) burned, 65 years of age and older with burn surgery for wound closure;More than 20% TBSA burned, 0–64 years of age with burn surgery for wound closure; Electrical high voltage/lightning injury with burn surgery for wound closure; or Hand burn and/or face burn and/or feet burn with burn surgery for wound closurePrevious inclusion criteria can be found at the bottom of this page: https://burndata.washington.edu/standard-operating-procedures/In 2015, the BMS began a major initiative to collect data every five years after the injury and to collect new psychometrically sound, patient-reported outcome measures. On December 31, 2024, the database contained information for 5,209 adults (18 years of age and older at the time of burn) and 2,447 children (17 years of age and younger at the time of burn). The BMS program disseminates evidence-based information to patients, family members, health care providers, educators, policymakers, and the general public. The BMS centers provide information in many ways: peer-reviewed publications, presentations at national professional meetings, fact sheets about different aspects of living with a burn injury, newsletters for patients on BMS research and center events, outreach satellite clinics for patients living in rural areas, and peer-support groups. The BMS program also collaborates with the NIDILRR-funded Model Systems Knowledge Translation Center to promote the adoption of research findings by rehabilitation professionals, policymakers, and persons with burn injuries and their family members. The BMS program establishes partnerships to increase the overall impact of research; information dissemination; and training of clinicians, researchers, and policymakers. Current partners include the American Burn Association (ABA) and the Phoenix Society. Together, these partners help the BMS to ensure that NIDILRR-funded research addresses issues that are relevant to people with burn injuries.