The State Lands Commission has prepared the Significant Lands Inventory (report) for the California Legislature as a general identification and classification of those unconveyed State school lands and tide and submerged lands which possess significant environmental values. The publication incorporates evaluated and pertinent comments received on the initial draft report which was circulated statewide in February 1975. The absence of a particular digitized waterway in the dataset does not mean that the State does not claim ownership of that parcel or waterway, or that such specific parcel or waterway has no significant environmental values. This dataset is not intended to establish ownership, only to identify those parcels which possess significant environmental values. Staff was unable to physically inventory all of the considered lands; instead, the advice and participation of those with known environmental expertise was utilized as additional to staff survey. · Tide and submerged lands are digitized in the WaterBody and WaterLine feature classes; WaterLines for coastal areas, WaterBody for inland areas. Tide and submerged lands under the jurisdiction of the State Lands Commission are those sovereign lands received from the Federal Government by virtue of California's admission to the Union on an equal footing with the original States. Such lands, and State interest therein, are generally the lands waterward of the ordinary high water mark of the Pacific Ocean (seaward to a three-mile limit); tidal bays, sloughs, estuaries; and, navigable lakes and streams within the State. · School Lands are digitized in the SchoolLand feature class. State school lands under the jurisdiction of the Commission are largely composed of the 16th and 36th sections of each township. The Federal Government transferred these lands to the State in 1853, in order to establish a financial foundation for a public school system. In cases where the 16th and 36th sections were mineral in character, incomplete as to acreage total, or already claimed or granted by the Federal Government, the State was permitted to select other lands "in lieu" of the specific sections. The public trust of commerce, navigation and fisheries which the State retains on patented sovereign lands should also be considered included in this inventory. Wherever a waterway, or body of water, is listed or mapped, the common trust state interest in patented sovereign lands, if any, is also included. The State Lands Commission emphasized when it adopted this report at its December 1, 1975 meeting that all tide and submerged lands are significant by the nature of their public ownership. Only because of the methodology used for this report are all of these waterways not specifically listed in this inventory. It is the intent of the State Lands Commission that the Significant Lands Inventory be periodically updated. This dataset should be considered informational, to assist the Legislature, the Commission, and the public in considering the environmental aspects of a proposed project and the significant values to be protected therein.

Each year the California Department of Finance surveys major group quarters living arrangements in support of population estimates and projections. A key part of this are Colleges and Universities, which often have unique allocation patterns that are not necessarily consistent with a centralized campus. The data presented here represents a selection of major private colleges, UCs and CSUs. Points are sourced from webpage scrubbing, address geocoding, and survey responses from related operations such as the Post-Census Group Quarters Review. Because of the mixed nature or record retrieval, we make no claims to the completeness of this data. Rather, we encourage feedback and engagement to continually improve this dataset. Please reach out to fennis.reed@dof.ca.gov with any inquiries.

Post-Secondary Employment Outcomes (PSEO) are experimental tabulations developed by researchers at the U.S. Census Bureau. PSEO data provide earnings and employment outcomes for college and university graduates by degree level, degree major, and post-secondary institution. These statistics are generated by matching university transcript data with a national database of jobs, using state-of-the-art confidentiality protection mechanisms to protect the underlying data. The Flows endpoint provides access to the destination industry and geography of employment for graduates of an institution by degree level, degree field, and graduation cohort, for one, five, and 10 years after graduation. PSEO data will be updated as new cells are able to be published.

The academic discipline of geography, faced with increasing competition from cognate fields and declining undergraduate enrollments, continues to suffer an identity crisis. In recent decades, many geography programs have instituted department or degree name changes, or otherwise rebranded, without any evidence guiding these decisions. This study begins to build an evidence base for these decisions by presenting results from a survey of 4,388 undergraduates across four U.S. universities to understand how students rate key words that commonly appear in geography course descriptions and titles and phrases that comprise degree and department names. Undergraduates overwhelmingly and consistently preferred simple, thematic types of terms to those that sounded more technical or science oriented. Forms of the word geography were rated significantly lower than words or phrases containing environment and sustainability. Forms of geography that included the word science were rated particularly low. Student ratings varied by class standing, major, gender, high school location (United States vs. outside of the United States), whether the student had previously enrolled in a geography course, and self-perceived numeracy. Multivariable analysis revealed potential opportunities for targeted undergraduate recruiting and curricular development. This study is an important step toward reconciling contemporary student perceptions of geography and related fields with departmental identities and the disciplinary jargon often used in program and course descriptions. We offer a toolkit for implementing similar research at other institutions and ultimately helping geography programs recruit and retain the next generation of geographers.

This map shows the percent of adults (25+) who have completed some college, but do not hold a degree - ideal places to implement college completion programs. For example, Texas implemented a state-wide program called GradTX aimed at helping stop-outs finish what they started. Other states such as Colorado and Florida have reverse transfer programs. An all-too common situation is that students take enough credits to successfully transfer from a 2-year college to a 4-year college, but do not attain an associate degree. Life happens and these transfer students do not graduate from the 4-year college, however, they have completed an additional semester or more of college credits. Reverse transfer programs allow these students to transfer these credits earned at the 4-year college back to the 2-year college in order to receive an associate degree. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

The percentage of persons that have completed, graduated, or received a high school diploma or GED and also have taken some college courses or completed their Associate's degree. This is a standard indicator used to measure the portion of the population with a basic level of skills needed for the workplace. Persons under the age of 25 are not included in this analysis since many of these persons are still attending various levels of schooling. Source: American Community Survey Years Available: 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

Compares distribution of highest certificate, diploma or degree between provinces and territories. Allows sorting/ranking of provinces and territories by percentage.

Data on the top universities for Social Sciences in 2025, including disciplines such as Communication & Media Studies, Geography, and Sociology.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

The Schools data set is a point locator of K-12 public, charter and private schools along with major college and Universities within the City of Sacramento's Policy Area. It is maintained through individual research and contact with school districts.

This map shows the percentage of people age 25+ whose highest education level is a high school degree. This is shown by state, county, and census tracts throughout the US. Zoom to any city to see the pattern there, or use one of the bookmarks to explore different areas. For more information about the education attainment breakdown from the US Census Bureau, click here.The pop-up is configured to show the overall breakdown of educational attainment for the population 25+. The data shown is current-year American Community Survey (ACS) data from the US Census Bureau. The data is updated each year when the ACS releases its new 5-year estimates. For more information about the data, visit this page.To learn more about when the ACS releases data updates, click here.

A detailed explanation of how this dataset was put together, including data sources and methodologies, follows below.Please see the "Terms of Use" section below for the Data DictionaryDATA ACQUISITION AND CLEANING PROCESSThis dataset was built from 5 separate datasets queried during the months of April and May 2023 from the Census Microdata System (link below):https://data.census.gov/mdat/#/All datasets include information on Property Value (VALP) by: Educational Attainment (SCHL), Gender (SEX), a specified race or ethnicity (RAC or HISP), and are grouped by Public Use Microdata Areas (PUMAS). PUMAS are geographic areas created by the Census bureau; they are weighted by land area and population to facilitate data analysis. Data also Included totals for the state of New Mexico, so 19 total geographies are represented. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Datasets were downloaded separately by race and ethnicity because this was the only way to obtain the VALP, SCHL, and SEX variables intersectionally with race or ethnicity data. Cleaning each dataset started with recoding the SCHL and HISP variables - details on recoding can be found below.After recoding, each dataset was transposed so that PUMAS were rows and SCHL, VALP, SEX, and Race or Ethnicity variables were the columns.Median values were calculated in every case that recoding was necessary. As a result, all Property Values in this dataset reflect median values.At times the ACS data downloaded with zeros instead of the 'null' values in initial query results. The VALP variable also included a "-1" variable to reflect N/A values (details in variable notes). Both zeros and "-1" values were removed before calculating median values, both to keep the data true to the original query and to generate accurate median values.Recoding the SCHL variable resulted in 5 rows for each PUMA, reflecting the different levels of educational attainment in each region. Columns grouped variables by race or ethnicity and gender. Cell values were property values.All 5 datasets were joined after recoding and cleaning the data. Original datasets all include 95 rows with 5 separate Educational Attainment variables for each PUMA, including New Mexico State totals.Because 1 row was needed for each PUMA in order to map this data, the data was split by Educational Attainment (SCHL), resulting in 110 columns reflecting median property values for each race or ethnicity by gender and level of educational attainment.A short, unique 2 to 5 letter alias was created for each PUMA area in anticipation of needing a unique identifier to join the data with. GIS AND MAPPING PROCESSA PUMA shapefile was downloaded from the ACS site. The Shapefile can be downloaded here: https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/PUMA_TAD_TAZ_UGA_ZCTA/MapServerThe DBF from the PUMA shapefile was exported to Excel; this shapefile data included needed geographic information for mapping such as: GEOID, PUMACE. The UIDs created for each PUMA were added to the shapefile data; the PUMA shapfile data and ACS data were then joined on UID in JMP.The data table was joined to the shapefile in ARC GiIS, based on PUMA region (specifically GEOID text).The resulting shapefile was exported as a GDB (geodatabase) in order to keep 'Null' values in the data. GDBs are capable of including a rule allowing null values where shapefiles are not. This GDB was uploaded to NMCDCs Arc Gis platform. SYSTEMS USEDMS Excel was used for data cleaning, recoding, and deriving values. Recoding was done directly in the Microdata system when possible - but because the system is was in beta at the time of use some features were not functional at times.JMP was used to transpose, join, and split data. ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform. VARIABLE AND RECODING NOTESTIMEFRAME: Data was queried for the 5 year period of 2015 to 2019 because ACS changed its definiton for and methods of collecting data on race and ethinicity in 2020. The change resulted in greater aggregation and les granular data on variables from 2020 onward.Note: All Race Data reflects that respondants identified as the specified race alone or in combination with one or more other races.VARIABLE:ACS VARIABLE DEFINITIONACS VARIABLE NOTESDETAILS OR URL FOR RAW DATA DOWNLOADRACBLKBlack or African American ACS Query: RACBLK, SCHL, SEX, VALP 2019 5yrRACAIANAmerican Indian and Alaska Native ACS Query: RACAIAN, SCHL, SEX, VALP 2019 5yrRACASNAsian ACS Query: RACASN, SCHL, SEX, VALP 2019 5yrRACWHTWhite ACS Query: RACWHT, SCHL, SEX, VALP 2019 5yrHISPHispanic Origin ACS Query: HISP ORG, SCHL, SEX, VALP 2019 5yrHISP RECODE: 24 original separate variablesThe Hispanic Origin (HISP) variable originally included 24 subcategories reflecting Mexican, Central American, South American, and Caribbean Latino, and Spanish identities from each Latin American counry. 7 recoded VariablesThese 24 variables were recoded (grouped) into 7 simpler categories for data analysis: Not Spanish/Hispanic/Latino, Mexican, Caribbean Latino, Central American, South American, Spaniard, All other Spanish/Hispanic/Latino Female. Not Spanish/Hispanic/Latino was not really used in the final dataset as the race datasets provided that information.SCHLEducational Attainment25 original separate variablesThe Educational Attainment (SCHL) variable originally included 25 subcategories reflecting the education levels of adults (over 18) surveyed by the ACS. These include: Kindergarten, Grades 1 through 12 separately, 12th grade with no diploma, Highschool Diploma, GED or credential, less than 1 year of college, more than 1 year of college with no degree, Associate's Degree, Bachelor's Degree, Master's Degree, Professional Degree, and Doctorate Degree.SCHL RECODE: 5 recoded variablesThese 25 variables were recoded (grouped) into 5 simpler categories for data analysis: No High School Diploma, High School Diploma or GED, Some College, Bachelor's Degree, and Advanced or Professional DegreeSEXGender2 variables1 - Male, 2 - FemaleVALPProperty Value1 variableValues were rounded and top-coded by ACS for anonymity. The "-1" variable is defined as N/A (GQ/ Vacant lots except 'for sale only' and 'sold, not occupied' / not owned or being bought.) This variable reflects the median value of property owned by individuals of each race, ethnicity, gender, and educational attainment category.PUMAPublic Use Microdata Area18 PUMAsPUMAs in New Mexico can be viewed here:https://nmcdc.maps.arcgis.com/apps/mapviewer/index.html?webmap=d9fed35f558948ea9051efe9aa529eafData includes 19 total regions: 18 Pumas and NM State TotalsNOTES AND RESOURCESThe following resources and documentation were used to navigate the ACS PUMS system and to answer questions about variables:Census Microdata API User Guide:https://www.census.gov/data/developers/guidance/microdata-api-user-guide.Additional_Concepts.html#list-tab-1433961450Accessing PUMS Data:https://www.census.gov/programs-surveys/acs/microdata/access.htmlHow to use PUMS on data.census.govhttps://www.census.gov/programs-surveys/acs/microdata/mdat.html2019 PUMS Documentation:https://www.census.gov/programs-surveys/acs/microdata/documentation.2019.html#list-tab-13709392012014 to 2018 ACS PUMS Data Dictionary:https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2014-2018.pdf2019 PUMS Tiger/Line Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Public+Use+Microdata+Areas Note 1: NMCDC attemepted to contact analysts with the ACS system to clarify questions about variables, but did not receive a timely response. Documentation was then consulted.Note 2: All relevant documentation was reviewed and seems to imply that all survey questions were answered by adults, age 18 or over. Youth who have inherited property could potentially be reflected in this data.Dataset and feature service created in May 2023 by Renee Haley, Data Specialist, NMCDC.

This is a 24x24 inch downloadable PDF map of education centers, elementary, middle, and high schools, and local colleges in Broward County. Includes combined elementary and middle school facilities, combined middle and high school facilities.

Layers include: Broward Schools.

Using the latest available data from the U.S. Census Bureau's American Community Survey (ACS), this map examines the housing own/rent decision of people with a college degree (bachelor's degree or higher). While the general pattern is that college graduates end up buying a home at some point in their careers, the map reveals which neighborhoods actually have more renters than home owners, among college graduates.The map's topic is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. To see the full list of attributes available in this map's layers, go to a layer listed under the "Layers" section below and choose the "Data" tab for that layer, and choose "Fields" at the top right on that page. Current Vintage: 2018-2022ACS Table(s): B25013Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 7, 2023National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2022 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.Web Map originally compiled by Jim Herries

For the category for less than high school education, the highest level of education attained includes less than ninth grade education, as well as ninth to twelfth grade education without a high school diploma. For the high school graduates category, the highest level of education attained includes high school diploma or equivalency. For the some college education category, the highest level of education attained includes associate’s degree or some college education without a degree. For the bachelor's degree or higher category, the highest level of education attained includes bachelor’s, graduate, or professional degree. Due to rounding, educational attainment categories may not sum to 100%. Educational attainment is an important driver of life expectancy, as people with higher levels of education are more likely to obtain well-paying jobs, live in safer neighborhoods, have access to quality healthcare, and engage in healthier behaviors.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This layer shows education level for adults (25+) by veteran status. This is shown by county boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of veteran civilians age 25+ who have some college or an associate's degree as their highest education level.

Extents show participating schools in SBCTA's Safe-Routes-to-Schools (SRTS) Plan. Locations and attributes pulled from SBCTA's 'sbgis.DBO.Schools' feature class, with a metadata description indicating "...Schools as downloaded from the County on 11/24/2017". Some sites may have been added/removed since the date referenced here. Attributes include:Short and Long Name of school site.Grade levels served at school site.Jurisdiction of school. District of school.These sites have been adopted by SBCTA's Board September 2020, as required for inclusion in the San Bernardino County Active Transportation Plan (SBCATP, formally known as Non-Motorized Transportation Plan, or NMTP). Extents or attributes are not subject to major updates from when the Board last approved of the data until the next time SBCTA requests updates. Data however may be updated as needed for technical corrections to ensure data is accurate between major updates. If seeking the most current SRTS site status, contact the local jurisdiction/agency based on where a feature in question is shown located by this data. Not all sites within a jurisdiction may have been caputred by this data. Data is supplied as part of semi-regular updates to SBCTA's SBCATP. The SBCATP conforms to the requirements established by the State of California for local jurisdiction eligibility to receive grant funds through ATP-cycle grants, formally under the State Bicycle Transportation Account (BTA).Visit the following web address to see data in use:gosbcta.com/activesanbernardino : Active San Bernardino Data dynamic story maps site