The number of persons employed by businesses (both for-profit and non-profit) that provide products and services to local residents. The industries included in this indicator are: Retail Trade (NAICS 44-45); Finance and Insurance (NAICS 52); Professional, Scientific, and Technical Services (NAICS 54); Health Care and Social Assistance (NAICS 62); Arts, Entertainment, and Recreation (NAICS 71); Accommodation and Food Services (NAICS 72); and Other Services except Public Administration (NAICS 81). The primary industry reported by each business was used to determine their inclusion. The persons employed by these businesses may not necessarily live in the neighborhood where the business is located. Source: InfoUSA Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

The Geospatial Analytics Market size was valued at USD 98.93 billion in 2023 and is projected to reach USD 227.04 billion by 2032, exhibiting a CAGR of 12.6 % during the forecasts period. The Geospatial Analytics Market describes an application of technologies and approaches processing geographic and spatial data for intelligence and decision-making purposes. This market comprises of mapping tools and software, spatial data and geographic information systems (GIS) used in various fields including urban planning, environmental, transport and defence. Use varies from inventory tracking and control to route optimization and assessment of changes in environment. Other trends are the growth of big data and machine learning to improve the predictive methods, the improved real-time data processing the use of geographic data in combination with other technologies, for example, IoT and cloud. Some of the factors that are fuelling the need to find a marketplace for GIS solutions include; Increasing importance of place-specific information Increasing possibilities for data collection The need to properly manage spatial information in a high stand environment. Recent developments include: In May 2023, Google launched Google Geospatial Creator, a powerful tool that allows users to create immersive AR experiences that are both accurate and visually stunning. It is powered by Photorealistic 3D Tiles and ARCore from Google Maps Platform and can be used with Unity or Adobe Aero. Geospatial Creator provides a 3D view of the world, allowing users to place their digital content in the real world, similar to Google Earth and Google Street View. , In April 2023, Hexagon AB launched the HxGN AgrOn Control Room. It is a mobile app that allows managers and directors of agricultural companies to monitor all field operations in real time. It helps managers identify and address problems quickly, saving time and money. Additionally, the app can help to improve safety by providing managers with a way to monitor the location and status of field workers. , In December 2022, ESRI India announced the availability of Indo ArcGIS offerings on Indian public clouds and services to provide better management, collecting, forecasting, and analyzing location-based data. , In May 2022, Trimble announced the launch of the Trimble R12i GNSS receiver, which has a powerful tilt adjustment feature. It enables land surveyors to concentrate on the task and finish it more quickly and precisely. , In May 2021, Foursquare purchased Unfolded, a US-based provider of location-based services. This US-based firm provides location-based services and goods, including data enrichment analytics and geographic data visualization. With this acquisition, Foursquare aims to provide its users access to various first and third-party data sets and integrate them with the geographical characteristics. , In January 2021, ESRI, a U.S.-based geospatial image analytics solutions provider, introduced the ArcGIS platform. ArcGIS Platform by ESRI operates on a cloud consumption paradigm. App developers generally use this technology to figure out how to include location capabilities in their apps, business operations, and goods. It aids in making geospatial technologies accessible. .

Represents the number of employees occupying positions, by Portfolio, Department and Branch. These totals are different from Full Time Equivalent (FTE) totals. Headcount may exceed the budgeted position count if there are casual or part-time employees in the branch. For example, in Parks and Recreation there may be 10 part-time lifeguards associated with 1 FTE. Each lifeguard would work 4 hours per week, which equates to 1 FTE (based on a 40-hour work week). This occurs throughout the organization.

Accuracy: There are no known errors in the data. The data in the Report is taken at one point in time and while the information is useful to show trends and job functions, due to the number of on-going organizational and position changes, the information will become dated as the year progresses. Organizational changes from one year to the next may also make year-over-year comparisons difficult.

Update Frequency:n/a

Contact: Robert Vincent

This feature layer contains Esri demographics including population, spending, and business data (NAICS code) that is related to people or businesses that could be positively or negatively impacted by the novel coronavirus (COVID-19). Data vintage is 2019 and available from country to block group standard geographies, with the addition of congressional districts and places. Esri's Business Summary Data: Esri's Business Locations data is extracted from a comprehensive list of businesses licensed from Infogroup. It summarizes the comprehensive list of businesses from Infogroup for select NAICS and SIC summary categories by geography and includes total number of businesses, total sales, and total number of employees for a trade area.Esri's U.S. 2019 Data: Population, age, income, race, home value, spending, business, and market potential are among the topics included in the data suite. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies. To browse, all data variables available within Esri's demographics explore the Data Browser. Additional Esri Resources:Get StartedEsri DemographicsU.S. 2019 Esri Updated DemographicsBusiness Summary DataMethodologies

Historical information about the total Employees and Businesses Dataset is a snapshot of the total number of businesses that are currently in Mesa, as well as the total number of employees that work in Mesa. Source: ESRI Community Analyst. It is important to note that in this dataset, a “Full-Time Employee (FTE)” in Mesa is someone who may not necessarily live in Mesa, however, they are employed at a business that is located in Mesa. This is a distinct difference between the “Employment” number in Mesa, which is stated in the “Employment Dataset.” Employment refers to the total number of Mesa residents that are employed, within or outside of the City of Mesa.

This layer shows workers' place of residence by mode of commute. 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. This layer is symbolized by the percentage of workers who drove alone. 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): B08301 (Not all lines of this ACS table are available in this feature layer.)Data 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.

The number of persons employed in the creative economy (both for-profit and non-profit). This number does not count those persons who identify themselves as being artists and does not count sole proprietorships or persons who work part-time in the arts. The same industries used to calculate the rate businesses in the creative economy are used to calculate total employment in the creative economy. Source: InfoUSA Years Available: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023

This layer contains data on the number of employees and the number of establishments for selected 2-digit North American Industry Classification System (NAICS) codes from the the United States Census Bureau's County Business Patterns Program (CBP). This is shown by District boundaries. The full CBP data set (available at census.gov) is updated annually to contain the most currently released CBP data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Current Vintage: 2022 CBP Table: CB2000CBP. Data downloaded from: Census Bureau's API for County Business Patterns. Date of API call: January 2, 2025. Please cite the Census Bureau and CBP when using this data. Data Processing Notes: Boundaries come from the US Census Bureau TIGER geodatabases. 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 Bureau. Downloaded data processed by the Office of Planning on R statistical software and ESRI ArcGIS Desktop. Blank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov for details on these withheld records.

The number of persons employed by businesses (both for-profit and non-profit) that provide products and services to local residents. The industries included in this indicator are: Retail Trade (NAICS 44-45); Finance and Insurance (NAICS 52); Professional, Scientific, and Technical Services (NAICS 54); Health Care and Social Assistance (NAICS 62); Arts, Entertainment, and Recreation (NAICS 71); Accommodation and Food Services (NAICS 72); and Other Services except Public Administration (NAICS 81). The primary industry reported by each business was used to determine their inclusion. The persons employed by these businesses may not necessarily live in the neighborhood where the business is located. Source: InfoUSA Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

The total number of jobs per neighborhood. This indicator only counts jobs that are currently held by employees. Source: U.S. Census Bureau, Longitudinal Employer-Household Dynamics Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022

Database file was created to support The Office of Economic Development ArcGIS.com application and to use in conjunction with ESRI Business Online Analyst. The data was provided by Data Axle in February 2023. The purpose of the map is to view business data in relation to local zoning and land use, aerial photography, and relevant demographic data.By mining multiple data sources, Data Axle delivers a database that is that is the most robust and comprehensive available. We compile our databases from a number of sources including: GIS projects require the use of comprehensive data sets in order to perform a detailed analysis. Data Axle is widely recognized as providing the most comprehensive solution in the market – with more than 160+ business data elements to select from. A sample listing of the type of attributes collected are listed below.• Hundreds of county-level public sources, publications of record and Secretaries of State for new business registrations• Utility connects and disconnects nationwide• Industry & tourism directories• User generated feedback• Postal processing (NCOALink®, DPV®, LACSLink®, DSF®)• 4,000+ U.S. Yellow and White Page directories• Business name• Full address (Location, mailing, and landmark)• Type of business (Yellow Page heading, SIC and NAICS codes)• ZIP Code™ (including ZIP + 4®)• Telephone number• Fax number (where available)• Website addresses• Number of employees• Sales volume• Name, title, and gender of key executives• Franchise and brand information• Year the business was established• Headquarters, branch, and subsidiary information• Stock exchange and ticker symbol• Latitude, longitude and parcel-level geocodes• News headlines• UCC filings and bankruptcy notices (where available)• Square footage of business campus

This dataset comes from the Biennial City of Tempe Employee Survey questions related to employee engagement. Survey respondents are asked to rate their level of agreement on a scale of 5 to 1, where 5 means "Strongly Agree" and 1 means "Strongly Disagree".This dataset includes responses to the following statements:I have received fair consideration for advancement & promotion, when available, within City of TempeI have been mentored at workThe City's programs related to professional development & career mobility, such as educational partnerships, Tempe Professional Development Network, etc., are useful to meThe following adequately support my work-related needs: City Manager's OfficeThe following adequately support my work-related needs: Strategic Management & Diversity OfficeI believe my opinions seem to countConflict in my work area is resolved effectivelyI believe exceptional job performance is recognized appropriately by managers/supervisors in my work unitThe amount that I pay for health care benefits is reasonableI think the amount I am paid is adequate for the work I doCommunication between my work unit/pision & work units/pisions OUTSIDE my department is goodEmployees in my department take personal accountability for their actions and work performance (starting in 2018 survey)Participation in the survey is voluntary and confidential.This page provides data for the Employee Engagement performance measure. The performance measure dashboard is available at 2.13 Employee Engagement.Additional InformationSource: paper and digital survey submissionsContact: Aaron PetersonContact E-Mail: Aaron_Peterson@tempe.govData Source Type: ExcelPreparation Method: NAPublish Frequency: biennialPublish Method: ManualData Dictionary

The Regional Municipality of Durham, Planning and Economic Development Department and its municipal partners conduct an annual count of businesses within the Region. Between May and September, the Region's Business Count team visit every business establishment in Durham to gather basic information about the type and nature of the business, the number of employees, floor space, etc. The success of this important survey depends upon the assistance and co-operating of the region's business community.

This map service was created to support the National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management’s (OCM) Coastal Flood Exposure Mapper. The purpose of the online mapping tool is to provide coastal managers, planners, and stakeholders a preliminary look at exposures to coastal flooding hazards. The Mapper is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help communities initiate resilience planning efforts. As with all remotely sensed data, all features should be verified with a site visit. The dataset is provided "as is," without warranty to its performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of this dataset is assumed by the user. This dataset should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes. For more information, visit the Coastal Flood Exposure Mapper (https://coast.noaa.gov/floodexposure).Send questions or comments to the NOAA Office for Coastal Management (coastal.info@noaa.gov).

The UK geospatial analytics market is projected to reach a value of USD 1.85 billion by 2033, expanding at a CAGR of 11.26% during the forecast period (2025-2033). The increasing demand for geospatial data for decision-making across various industry verticals, such as defense, intelligence, healthcare, and transportation, is driving market growth. The government's emphasis on smart city projects and the adoption of location-based services are also contributing to the market's expansion. Key market trends include the growing adoption of cloud-based geospatial analytics platforms, the increasing use of artificial intelligence (AI) and machine learning (ML) for geospatial data analysis, and the emergence of 5G technology, which enables real-time data collection and processing. The market is segmented by type (surface analysis, network analysis, geovisualization) and end-user vertical (agriculture, utility and communication, defense and intelligence, government, mining and natural resources, automotive and transportation, healthcare, real estate and construction). Key players in the UK geospatial analytics market include SAS Institute Inc, Trimble, General Electric, Accenture, Bluesky International Ltd, ESRI Inc, Oracle Corporation, Bentley Systems Inc, and Hexagon. Recent developments include: April 2023: EDF used Esri UK corporate GIS to build a geospatial site for the Hinkley Point C nuclear power station, one of Europe's most extensive and complicated building projects. The portal provides a single picture of the entire project. They are facilitating greater cooperation and enabling new digital workflows, Assisting employees and contractors in improving safety and productivity. When the building of the nuclear reactors began, the portal has recently been expanded to include Tier-1 contractors, and it presently has over 1,500 users., April 2021: Esri UK launched a new cooperation with Tetra Tech, a worldwide consulting and engineering services company, to enhance indoor mapping capabilities by combining their expertise. Esri UK was to contribute to the partnership's robust GIS system, which had multiple indoor mapping capabilities, such as interactive floor plans and indoor location capabilities. Tetra Tech was to add 3D terrestrial laser scanning, data analytics, and CAD capabilities to GIS. They were to collaborate to provide customers with an end-to-end interior mapping solution to capitalize on an expanding need for indoor mapping for facilities management at central workplaces, campuses, or hospitals.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Location data will hold the significant share.

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.The original source for these layers are: Business Data: InfoUSA business database purchased by DDP in 2017Building Data: Detroit Building Footprint data Parcel Data: from Detroit Open Data Portal, download in May 2018.For field research by Tian, some fields have been added and some records in building and business have been edited. For business data, Tian confirmed most of public assessable businesses and deleted those which do not exist. Also, Tian add new Business to the business data if it did not exist on the record. For building data, Tian recorded the total business space for each building, not-empty business space, occupancy status, parking adjacency status, and took picture for every building in downtown Detroit. Detail field META DATA:InfoUSA Business OBJECTID_1 COMPANY_NA: company nameADDRESS: company addressCITY: citySTATE: stateZIP_CODE: zip codeMAILING_CA: source InfoUSAMAILING_DE source InfoUSALOCATION_A source InfoUSA: addressLOCATION_1 source InfoUSA: cityLOCATION_2 source InfoUSA: stateLOCATION_3 source InfoUSA: zip codeLOCATION_4source InfoUSALOCATION_5 source InfoUSACOUNTY: countyPHONE_NUMB: phone numberWEB_ADDRES: website addressLAST_NAME: contact last nameFIRST_NAME: contact first nameCONTACT_TI: contact type CONTACT_PR: CONTACT_GE: contact genderACTUAL_EMP: employee numberEMPLOYEE_S: employee number classACTUAL_SAL: actual sale SALES_VOLU: sales value PRIMARY_SI: primary sales valuePRIMARY_1: primary classificationSECONDARY_: secondary classification SECONDARY1SECONDAR_1SECONDAR_2CREDIT_ALP: credit level CREDIT_NUM: credit numberHEADQUARTE: headquarteYEAR_1ST_A: year openOFFICE_SIZ: office sizeSQUARE_FOO: square footFIRM_INDIV:PUBLIC_PRI Fleet_size FRANCHISE_ FRANCHISE1 INDUSTRY_SADSIZE_IN_METRO_AREA INFOUSA_ID LATITUDE: yLONGITUDE: xPARKING: parking adjacency NAICS_CODE: NAICS CODENAICS_DESC: NAICS DESCRIPTION parcelnum*: PARCEL NUMBER parcelobji* PARCEL OBJECT IDCHECK_* ACCESSIABLE* PUBLIC ACCESSIBILITYPROPMANAGER* PROPERTY MANAGERGlobalID Notes: field with * means it came from other source or field research done by Tian Xie in Aug, 2018BuildingOBJECTID_12 BUILDING_I: building idPARCEL_ID : parcel id BUILD_TYPE: building type CITY_ID：city id APN: parcel number RES_SQFT: Res square feet NONRES_SQF non-res square feetYEAR_BUILT: year built YEAR_DEMOHOUSING_UN: housing unitsSTORIES: # of stories MEDIAN_HGT: median height CONDITION: building condition HAS_CONDOS: has condos or not FLAG_SQFT: flag square feet FLAG_YEAR_: flag yearFLAG_CONDI: flag condition LOADD1: address number HIADD1 (type: esriFieldTypeInteger, alias: HIADD1, SQL Type: sqlTypeOther, nullable: true, editable: true)STREET1: street name LOADD2: HIADD2 (type: esriFieldTypeString, alias: HIADD2, SQL Type: sqlTypeOther, length: 80, nullable: true, editable: true)STREET2 (type: esriFieldTypeString, alias: STREET2, SQL Type: sqlTypeOther, length: 80, nullable: true, editable: true)ZIPCODE: zip code AKA: building name USE_LOCATOTEMP (type: esriFieldTypeString, alias: TEMP, SQL Type: sqlTypeOther, length: 80, nullable: true, editable: true)SPID (type: esriFieldTypeInteger, alias: SPID, SQL Type: sqlTypeOther, nullable: true, editable: true)Zone (type: esriFieldTypeString, alias: Zone, SQL Type: sqlTypeOther, length: 60, nullable: true, editable: true)F7_2SqMile (type: esriFieldTypeString, alias: F7_2SqMile, SQL Type: sqlTypeOther, length: 10, nullable: true, editable: true)Shape_Leng (type: esriFieldTypeDouble, alias: Shape_Leng, SQL Type: sqlTypeOther, nullable: true, editable: true)PARKING*: parking adjacency OCCUPANCY*: occupied or not BuildingType* : building type TotalBusinessSpace*: available business space in this buildingNonEmptySpace*: non-empty business space in this buildingCHECK_* FOLLOWUP*: need followup or notGlobalID*PropmMana*: property manager Notes: field with * means it came from other source or field research done by Tian Xie in Aug, 2018

This model depicts driving time projections for the locations of all employers in the THRIVE 2055 project boundary, with 10 or more employees. ESRI streetmap North America data was used with point locations for employers, provided by the Southeast Tennessee Development District, to calculate service areas. Service areas were broken down into 3 categories, depicting 5, 10, & 15 minute drive time thresholds.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity.

The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions.

This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed.

See https://www.epa.gov/smartgrowth/smart-location-mapping for more information.

Represents the total number of active employees occupying positions, by Department and Collective Bargaining Unit. These totals are different from Full Time Equivalent (FTE) totals. Headcount may exceed the budgeted position count if there are casual or part-time employees in the position. For example, in Parks and Recreation there may be 10 part-time lifeguards associated with 1 FTE. Each lifeguard would work 4 hours per week, which equates to 1 FTE (based on a 40-hour work week). This occurs throughout the organization.

Accuracy: There are no known errors in the data. The data in the Report is taken at one point in time and while the information is useful to show trends and job functions, due to the number of on-going organizational and position changes, the information will become dated as the year progresses. Organizational changes from one year to the next may also make year-over-year comparisons difficult.

Update Frequency: Twice a Year – 30 June and 31 December

Contact: Melisa Lessard

The total number of businesses (both for-profit and non-profit) that report having less than 50 persons employed within an area at a single time in a year. Source: InfoUSA Years Available: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023