Explore the progression of average salaries for graduates in Surveying And Photogrammetry Geomatics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Surveying And Photogrammetry Geomatics relative to other fields. This data is essential for students assessing the return on investment of their education in Surveying And Photogrammetry Geomatics, providing a clear picture of financial prospects post-graduation.

Explore the progression of average salaries for graduates in Geographic Information Systems (Gis) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Geographic Information Systems (Gis) relative to other fields. This data is essential for students assessing the return on investment of their education in Geographic Information Systems (Gis), providing a clear picture of financial prospects post-graduation.

The Employee Salary dataset contains payroll and position data, including: position description, department, FLSA status, pay range, pay grade and step (if applicable), bargaining unit, and base salary information. Each row contains relevant data for a single employee or vacant position. A data dictionary with descriptions of the fields included in the dataset can be downloaded here. The core dataset is updated semi-annually and supporting files on an as-needed basis.Additional supporting files include: Salary bands for each pay grade and step (if applicable) for the four labor groups:Boulder Municipal Employee’s Association – BMEA International Association of Fire Fighters – IAFFBoulder Police Officer’s Association – BPOAManagement / Non-Union – MGMTList of currently used job classes by labor group and grade

Explore the progression of average salaries for graduates in Gis from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Gis relative to other fields. This data is essential for students assessing the return on investment of their education in Gis, providing a clear picture of financial prospects post-graduation.

Table contains median household income for households residing in Santa Clara County. Data are presented at county, city, zip code and census tract level. Notes: Data are presented for zip codes (ZCTAs) fully within the county. Data are capped at $250,001 for geographies with median household income of $250,000 or higher. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B19013; data accessed on May 16, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographymedHHinc (Numeric): Median household income

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

Explore the progression of average salaries for graduates in Remote Sensing/Gis from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Remote Sensing/Gis relative to other fields. This data is essential for students assessing the return on investment of their education in Remote Sensing/Gis, providing a clear picture of financial prospects post-graduation.

CDFW BIOS GIS Dataset, Contact: FAB Financial Assistance Branch, Description: This data contains summary information for Disadvantaged ($56,982) and Severely Disadvantaged ($42,737) communities. The thresholds are derived from American Community Survey 2014-18 (ACS 2014-18) 5-year estimates at the census place geographic level and the California State Median Household Income of $71,228.

General Mills reported $625.6M in Net Income for its fiscal quarter ending in February of 2025. Data for General Mills | GIS - Net Income including historical, tables and charts were last updated by Trading Economics this last March in 2025.

Table contains count and percentage of households with an annual household income of less than $100,000. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B19001; data accessed on May 16, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographytotalHH (Numeric): Total householdslt100k (Numeric): Number of households with less than $100,000 annual incomepct_lt100k (Numeric): Percent of households with less than $100,000 annual income

Explore the progression of average salaries for graduates in Geospatial Information Systems (Gis) from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Geospatial Information Systems (Gis) relative to other fields. This data is essential for students assessing the return on investment of their education in Geospatial Information Systems (Gis), providing a clear picture of financial prospects post-graduation.

Table from the American Community Survey (ACS) 5-year series on income and earning related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B19025 Aggregate Household Income, B19013 Median Household Income, B19001 Household Income, B19113 Median Family Household Income, B19101 Family Household Income, B19202 Median Nonfamily Household Income, B19201 Nonfamily Household Income, B19301 Per Capita Income/B19313 Aggregate Income/B01001 Sex by Age, C24010 Sex by Occupation of the Civilian Employed Population 16 years and Over, B20017 Median Earnings by Sex by Work Experience for the Population 16 years and over with Earnings, B20001 Sex by Earnings for the Population 16 years and over with Earnings. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.

BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences. Geo-Analytical Factors Each store in your database is further enhanced with geo-analytical data. We analyze: Maximum pedestrian and vehicle traffic within a defined radius Number of households and average income within the catchment area vicinity Number of schools, hospitals, universities, competitors, stores, bars, clubs, and restaurants in the surrounding area Point attendance based on mobile device location data (ensuring GDPR compliance) Our refined retail data collection and analysis provides detailed shopping behavior insights, leading to in-depth shopper analytics and retail foot traffic data that support strategic planning and execution. The Power of Points of Interest (POI) Data At BestPlace, we harness the power of Point of Interest (POI) data (to bring you the most complete retail data set.) to bring your retail data to life. Our POI data collection process involves analyzing and categorizing foot traffic data, providing a comprehensive foot traffic dataset as a result. This data allows you to understand the ebb and flow of individuals around your store locations, suggesting invaluable insights for strategic planning and operational efficiency. Leveraging GIS Data Our GIS data collection process is meticulous and comprehensive. We tap into multiple GIS data sources, providing a wealth of data to enhance your retail analytics. This process allows us to equip your database with a broad range of geospatial features, including demographic and socioeconomic information from various census data for GIS applications. By including GIS data in your analysis, you gain a multi-dimensional perspective of your retail landscape, allowing for more strategic decision-making. The Advantages of Census Data BestPlace grants you direct access to a wealth of census data sets. This transforms your retail database into a more potent tool for decision-making, providing a deeper understanding of the demographics and socioeconomic factors surrounding your store locations. With the ability to download census data directly, you can enrich your retail data analysis with valuable insights about potential customers, giving you the upper hand in your strategic planning. Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics. Location Comparison: Compare multiple store locations based on numerous factors and make informed decisions about where to focus your resources. Distribution Optimization: Leverage our FMCG data analytics and retail traffic analytics to optimize your distribution strategy and maximize ROI. Building Machine Learning Models: Integrate our all-purpose machine learning models into your business decision processes to enable more efficient and effective decision-making. (Integrate our all-purpose machine learning models to build your own in-house solutions with the help of our data.) Comprehensive Deliverables As a BestPlace client, you receive a comprehensive produc...

This layer shows median household income by race and by age of householder. This 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. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National 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 2023 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.

Classification establishes and maintains the County's job classifications and compensation systems and practices, with equity, consistency, and due regard for pay competitiveness. Positions are analyzed and assigned to appropriate job classificationsJob Descriptions -- Frequently Asked Questions -- Classification and Compensation Documents

CI: confident interval;Percentage was derived by the number of geographic units for low income level divided by the total number of counties at each geographic division.

Link to Lake County, IL website.

General Mills reported $4.8B in Sales Revenues for its fiscal quarter ending in December of 2024. Data for General Mills | GIS - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last March in 2025.

Explore the progression of average salaries for graduates in Transportation And Gis from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Transportation And Gis relative to other fields. This data is essential for students assessing the return on investment of their education in Transportation And Gis, providing a clear picture of financial prospects post-graduation.