• Context I am greatly inspired with this dataset containing geo spatial details for each zip code and contains the total wages for each area.This gave me opportunity to create a data visualisation in Tableau using HexBin chart which is added as a Kernel to this dataset.

• Content

  • About the data: 81,831 rows of data. All 41,891 active zipcodes + 634 decommisioned zipcodes from the recent past. All 80673 active Primary(41885), Acceptable(13988), and Not Acceptable(24800) placenames. Some additonal placenames for decommisioned codes. 29,971 Standard, 9465 PO BOX, 2437 Unique, and 649 Military codes.

• 50 States + 361 AA Military

• Americas 38 AE Military

• Europe 164 AP Military

• Pacific 1 AS American Samoa 290 DC Washinton DC 4 FM Federated States Micronesia 13 GU Guam 2 MH Marshall Islands 3 MP Northern Mariana Islands 176 PR Puerto Rico 2 PW Palau 16 VI Virgin Islands

• Name Type Description

• Zipcode Text 5 digit Zipcode or military postal code(FPO/APO)

• ZipCodeType Text Standard, PO BOX Only, Unique, Military(implies APO or FPO)

• City Text USPS offical city name(s)

• State Text USPS offical state, territory, or quasi-state (AA, AE, AP) abbreviation code

• LocationType Text Primary, Acceptable,Not Acceptable

• Lat Double Decimal Latitude, if available

• Long Double Decimal Longitude, if available

• Location Text Standard Display (eg Phoenix, AZ ; Pago Pago, AS ; Melbourne, AU )

• Decommissioned Text If Primary location, Yes implies historical Zipcode, No Implies current Zipcode; If not Primary, Yes implies Historical Placename

• TaxReturnsFiled Long Integer Number of Individual Tax Returns Filed in 2008

• EstimatedPopulation Long Integer Tax returns filed + Married filing jointly + Dependents

• TotalWages Long Integer Total of Wages Salaries and Tips

  • Source: http://federalgovernmentzipcodes.us/

  • Current zipcodes, placenames, zipcode type(Standard, PO, Unique, Military), placename type (Primary, Acceptable, Not Acceptable)

• : USPS Military place names (base or ship name)

• : MPSA 2008 Election Ballot information Tax returns filed, estimated population, total wages: IRS 2008 Latitude and Longitude; National Weather Service supplemented by Google Earth and Maps and occasionally other sources Decommissioned zip codes, Our old database--usually quality sources, but not verifiable.

• Acknowledgements

  Other Sources of zipcode information:

• Placenames (Cities, towns, geographic features) can be found at US Geological Survey GNIS Dataset The IRS has additional data fields for 2008 and is reviewing their publication procedures for later years.

  see http://www.irs.gov/taxstats/indtaxstats/article/0,,id=96947,00.html

• The Census publishes data, but they use Zipcode Tabulation Areas (ZCTAs) which

• 1) have changed areas between the 2000 census and the 2010 census

• 2) do not map well to USPS zipcodes well. If needed http://www.census.gov/geo/ZCTA/zcta.html Social Security recipients by zipcode http://www.ssa.gov/policy/docs/statcomps/oasdi_zip/ For economic researchers and those who want tons of background on data sources by zipcode, University of Missouri OSEDA project

  • Free Zipcode Database (8.7 MB) Updated 1/22/2012 All Locations (Multiple locations for some zipcodes)

  Inspiration I am hoping that people in the right government agencies/authorities would be able to utilize this data to focus on

  community developments where it needs immediate attention.

This layer shows an approximation of the percent of households in the US might see a full refund check from the stimulus bill being passed to help deal with the COVID-19 economic impact. The criteria used for this map was:The amount of the credit allowed by subsection (a) (determined without regard to this subsection and subsection (e)) shall be reduced (but not below zero) by 5 percent of so much of the taxpayer’s adjusted gross income as exceeds—‘‘(1) $150,000 in the case of a joint return,‘‘(2) $112,500 in the case of a head of household, and‘‘(3) $75,000 in the case of a taxpayer not described in paragraph (1) or (2).‘‘(d) ELIGIBLE INDIVIDUAL.—For purposes of thissection, the term ‘eligible individual’ means any individualother than—‘‘(1) any nonresident alien individual,‘‘(2) any individual with respect to whom a deduction under section 151 is allowable to another taxpayer for a taxable year beginning in the calendar year in which the individual’s taxable year beginsThe data comes from the IRS SOI Tax Stats by ZIP Code from 2017 tax returns. The calculation for this map was calculated using the following aggregated income categories:$1 to under $25,000$25,000 to under $50,000$50,000 to under $75,000$75,000 to under $100,000$100,000 to under $200,000$200,000 or moreThese were used to estimate how many individuals who filed tax returns would warrant a full $1,200 from the stimulus bill. This was based on if a filer was single, joint, or head of household. Because some of the cutoffs for the bill fall within these categories, they were split to estimate refunds that fall within that range.

A. SUMMARY This dataset contains sales tax collected in San Francisco for calendar years 2015 through the quarter preceding the most recent one. Sales tax is aggregated, or summed, using Zip Code boundaries. However, some geographic boundaries have been combined to maintain the anonymity of businesses based on Taxation Code Section 7056. See “How to use this dataset” below for more details on how the data has been aggregated. Sales tax is collected by businesses on many types of transactions and regulated by the California Department of Tax and Fee Administration.

B. HOW THE DATASET IS CREATED Data is collected by HDL. The data is then aggregated based on the criteria outlined in the "How to use this dataset" section.

C. UPDATE PROCESS This dataset will be updated quarterly.

D. HOW TO USE THIS DATASET This dataset can be used to analyze sales tax data over time across geographic boundary in San Francisco. Due to data privacy protection regulations for businesses, sales tax data is not available for all geographic boundary. For example, boundaries where there are less than 4 businesses paying sales tax or a single business that pays 80% or more of the total sales tax have been combined with neighboring geographic boundary to protect the confidentiality of affected businesses.

Because of this aggregation, some Zip Code groups in this dataset may change in future years as the number of businesses in a particular Zip Code change. The historical data changes based on audit findings and amended returns. If Zip Code groupings change, it will happen when the dataset is updated - on a quarterly basis. These new blocks will be backfilled to previous years. Additionally, business payers with multiple locations (for example chain stores) are excluded because sales tax cannot be tied back to the location where it was collected.

E. RELATED DATASETS Sales Tax by Supervisor District Sales Tax by Census Block Sales Tax by Analysis Neighborhood Sales Tax by Zip Code

This is a MD iMAP hosted service. Find more information at http://imap.maryland.gov. In 1993 - the Maryland Department of Planning and the Maryland State Highway Administration entered into a Data Base Usage Agreement with Bell Atlantic and Data Chromatics - Inc. to develop an enhanced street address map for Maryland. ZIP Code boundary area files were one of the products derived from this partnership. The resulting boundary area files were intended to improve the cartographic quality and accuracy of the ZIP code boundary area files derived from the U.S. Census Bureau's post 1990 Census TIGER\Line Files (based on the Census Bureau's ZIP code tabulation areas - ZCTAs). Subsequent iterative improvements to the ZIP Code boundary area files have been made using premise address information associated with mapped parcel records as provided in the Maryland Department of Planning's MdProperty View GIS tax map and parcel record DVD product. The resulting files are meant to serve as a good approximation"" of ZIP codes as polygons (which in reality they are not) but are not official ZIP Code maps and are not meant to be a substitute for any products offered by the U.S. Postal Service - the official source for ZIP code information. While there are no restrictions on their use we do recommend that they are best used with MdProperty View and that MDP makes no guarantee or warranty regarding the files. Last Updated: 2012Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_PoliticalBoundaries/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively the ""Data"") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about the Low-Income Housing Tax Credit Program visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low Income Tax Credit Program

This dataset combines annual files from 2005 to 2017 published by the IRS. ZIP Code data show selected income and tax items classified by State, ZIP Code, and size of adjusted gross income. Data are based on individual income tax returns filed with the IRS.

The data include items, such as:

• Number of returns, which approximates the number of households
• Number of personal exemptions, which approximates the population
• Adjusted gross income (AGI)
• Wages and salaries
• Dividends before exclusion
• Interest received

Enrichment and notes: - the original data sheets (a column per variable, a line per year, zipcode and AGI group) have been transposed to get a record per year, zipcode, AGI group and variable - the data for Wyoming in 2006 was removed because AGI classes were not correctly defined, making the resulting data unfit for analysis. - the AGI groups have seen their definitions change: the variable "AGI Class" was used until 2008, with various intervals of AGI; "AGI Stub" replaced it in 2009. We provided the literal intervals (eg. "$50,000 under $75,000") as "AGI Group" in each case to help the analysis. - the codes for each tax item have been joined with a dataset of variables to provide full names. - some tax items are available since 2005, others since more recent years, depending on their introduction date (available in the dataset of variables); as a consequence, the time range of the plots or graphs may vary. - the unit for amounts and AGIs is a thousand dollars.

License: https://www.usa.gov/copyrighted-government-works

An overview of tax collection in Louisville Metro/Jefferson County with each section broken down by the zip codes of the filing entities for the 2020 tax year. It is a static report that capture tax information as of December 31, 2021. The Louisville Metro Revenue Commission compiled the data according to addresses provided by the taxpayers on their OL-3 and W-1 returns. You can find the report here. All tax collection represented on the following graphs indicates business activity in Louisville/Jefferson County. However, the exact location of activity within Jefferson County may not be known if an out of state or out of county business address was provided by the taxpayer on the filed returns. Due to the volume of the data not all zip codes have been shown on the graphs, only those with the highest levels of activity. An Overview of the SlidesSlide 2 – Shows total number of overall OL customers and tax due and a breakdown by states. Slide 3 – Shows total number of Kentucky OL customers and tax due and a breakdown zip code. A breakdown by customer type and business type is included.Slide 4 – Shows total number of Jefferson County OL customers and tax due and a breakdown zip code. A breakdown by customer type and business type is included.Slide 5 – Shows total number of overall W-1 customers and tax due and a breakdown by states.Slide 6 – Shows total number of Kentucky W-1 customers and tax due and a breakdown zip code. A breakdown by customer type and business type is included. Slide 7 – Shows total number of Jefferson County W-1 customers and tax due and a breakdown zip code. A breakdown by customer type and business type is included.

Release Date: 2021-06-10.Release Schedule:.The data in this file come from the 2017 Economic Census. For information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll..For the Professional, Scientific, and Technical Services (54) and Other Services (except Public Administration) (81) sectors, data are presented for establishments subject to federal income tax only. No data provided for establishments exempt from federal income tax...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Annual payroll ($1,000).First-quarter payroll ($1,000).Number of employees..Each record includes a code which represents the enterprise support industry served...For Professional, Scientific, and Technical Services (54) and Other Services (except Public Administration) (81), data are published by Tax Status (Establishments subject to federal income tax) only. No data is provided for establishments exempt from federal income tax...Geography Coverage:.The data are shown for employer establishments at the U.S. level only. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown at the 2-digit 2017 NAICS code level for selected industries including Transportation and Warehousing (48-49), Information (51), Professional, Scientific, and Technical Services (54), Administrative and Support and Waste Management and Remediation Services (56), and Other Services (except Public Administration) (81). Data are also shown for 2017 NAICS code 551114. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.106: Railroad transportation and U.S. Postal Service are out of scope...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector00/EC1700ENTSUP.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

This service provides spatial data and information on Difficult Development Areas (DDAs) used for the Low Income Housing Tax Credit program. DDAs are designated by U.S. Department of Housing and Urban Development (HUD) and defined in statute as areas with high construction, land, and utility costs relative to its Area Median Gross Income (AMGI). DDAs in metropolitan areas are designated along Census ZIP Code Tabulation Area (ZCTA) boundaries. DDAs in non-metropolitan areas are designated along county boundaries. DDAs may not contain more than 20% of the aggregate population of metropolitan and non-metropolitan areas, which are designated separately.

Exempt organization information is extracted monthly from the Internal Revenue Service’s Business Master File. This is a cumulative file, and the data are the most recent information the IRS has for these organizations.

If you have any questions about the tax-exempt organizations or the content of the files, please contact TE/GE Customer Account Services toll-free line at 1-877-829-5500

State is determined from the filing address and generally represent the location of an organization’s headquarters, which may or may not represent the state(s) in which an organization has operations.

Records are sorted by Employer Identification Number (EIN).

This dataset is for Connecticut only.

The IRS exempt organization data have been accumulated since the inception of the tax-exempt statutes. A determination letter is issued to an organization upon the granting of an exemption and is considered valid throughout the life of the organization, as long as the organization complies with the provisions of its exemption. If an organization's exemption is revoked, an announcement to inform potential donors of the revocation is published in the Internal Revenue Bulletin. In addition, the organization’s name is removed from publicly accessible venues, including this file.

Updated nightly.

NOTE: Split-interest trusts are not included in this database.

FIELDS AVAILABLE All exempt organization records on this file will contain the following data fields: Column Name Contents EIN Employer Identification Number (EIN) NAME Primary Name of Organization ICO In Care of Name STREET Street Address CITY City STATE State ZIP Zip Code GROUP Group Exemption Number SUBSECTION Subsection Code AFFILIATION Affiliation Code CLASSIFICATION Classification Code(s) RULING Ruling Date DEDUCTIBILITY Deductibility Code FOUNDATION Foundation Code ACTIVITY Activity Codes ORGANIZATION Organization Code STATUS Exempt Organization Status Code TAX_PERIOD Tax Period ASSET_CD Asset Code INCOME_CD Income Code FILING_REQ_CD Filing Requirement Code PF_FILING_REQ_CD PF Filing Requirement Code ACCT_PD Accounting Period ASSET_AMT Asset Amount INCOME_AMT Income Amount (includes negative sign if amount is negative) REVENUE_AMT Form 990 Revenue Amount (includes negative sign if amount is negative) NTEE_CD National Taxonomy of Exempt Entities (NTEE) Code SORT_NAME Sort Name (Secondary Name Line)

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address.

A. SUMMARY This dataset contains sales tax collected in San Francisco for calendar years 2015 through the quarter preceding the most recent one. Sales tax is aggregated, or summed, using Analysis Neighborhoods. However, some geographic boundaries have been combined to maintain the anonymity of businesses based on Taxation Code Section 7056. See “How to use this dataset” below for more details on how the data has been aggregated. Sales tax is collected by businesses on many types of transactions and regulated by the California Department of Tax and Fee Administration.

B. HOW THE DATASET IS CREATED Data is collected by HDL. The data is then aggregated based on the criteria outlined in the "How to use this dataset" section.

C. UPDATE PROCESS This dataset will be updated quarterly.

D. HOW TO USE THIS DATASET This dataset can be used to analyze sales tax data over time across geographic boundary in San Francisco. Due to data privacy protection regulations for businesses, sales tax data is not available for all geographic boundary. For example, boundaries where there are less than 4 businesses paying sales tax or a single business that pays 80% or more of the total sales tax have been combined with neighboring geographic boundary to protect the confidentiality of affected businesses.

Because of this aggregation, some Analysis Neighborhood groups in this dataset may change in future years as the number of businesses in a particular Analysis Neighborhood change. The historical data changes based on audit findings and amended returns. If Analysis Neighborhood groupings change, it will happen when the dataset is updated - on a quarterly basis. These new blocks will be backfilled to previous years. Additionally, business payers with multiple locations (for example chain stores) are excluded because sales tax cannot be tied back to the location where it was collected.

A map of the Analysis Neighborhood boundaries can be found here.

E. RELATED DATASETS Sales Tax by Supervisor District Sales Tax by Census Block Sales Tax by Analysis Neighborhood Sales Tax by Zip Code

Clay County Tax Parcels with Tax Parcel Data SchemeElement GroupElem#Element NameData Field NameTypeWidthSourceSource FieldDomain NameLookup TableDescriptionClay County Elements7.1Parcel ID NumberPIDDouble10 (0 decimal places)Parcel FabricPIDxxxxxxxxx Numeric7.2Parcel ID TextPINText10Parcel FabricPARCELID or PINxxxxxxxxx Text without dividing periods.Identification Elements1.1Parcel Identification NumberCOUNTY_PINText22Parcel FabricPARCEL_IDxx.xxx.xxxx Text with dividing periods.1.2Parcel ID StateSTATE_PINText28Script"27027-" + [PIN]Add the county code and a dash (“27027-") to the beginning of the PIN string.Clay County Elements7.3RegionREGION IntegerShort, 5Parcel FabricREGIONIs a numeric value form 0 to 97.4CommentsCOMMENTSText254Parcel FabricCOMMENTSA field for free form comments.7.5Document NumberDOCUMENTIntegerLong, 10Parcel FabricDOCUMENTThe recordered document number that descibes the tax parcelTax and Survey Elements4.55Edit DateEDIT_DATEDate8Parcel FabricEDIT_DATEThis is intended to indicate the date of the initial entry or last substantial change to the Tax Parcel. 4.56Export DateEXP_DATEDate8ScriptDate when the data is exported.PLSS Elements6.1SectionSECTIONIntegerShortParcel FabricSectionNumberTwo digit number6.2TownshipTOWNSHIPIntegerShortParcel FabricTownshipNumberThree digit number6.3RangeRANGEIntegerShortParcel FabricRangeNumberTwo digit numberClay County Elements7.6Revenue Object IDRevObjIdDouble10 (0 decimal places)Tax SystemRevObjIdID Number in Tax System4.22Tax YearTAX_YEARIntegerShortTax SystemTAX_YEARYear Taxes are based4.10Taxpayer NameTAX_NAMEText100Tax SystemTAX_NAMETaxpayer Name4.11Taxpayer Address Line 1TAX_ADD_L1Text100Tax SystemTAX_ADD_L1Taxpayer Street Address4.12Taxpayer Address Line 2TAX_ADD_L2Text100Tax SystemTAX_ADD_L2Taxpayer City4.13Taxpayer Address Line 3TAX_ADD_L3Text100Tax SystemTAX_ADD_L3Taxpayer State4.14Taxpayer Address Line 4TAX_ADD_L4Text100Tax SystemTAX_ADD_L4Taxpayer ZipAdministration Elements5.2School DistrictSCHOOL_DSTText10Tax SystemSCHOOL_DSTSchoolDistrictSchool District Number5.3Watershed DistrictWSHD_DSTText50Tax SystemWSHD_DISTWatershedDistrictWatershed DistrictClay County Elements7.7Taxing AuthorityTAX_AUTHORText75Tax SystemTAX_AUTHORLocal government levying taxesTax and Survey Elements4.1LotLOTText30Tax SystemLOTLot Number From Deed4.2BlockBLOCKText30Tax SystemBLOCKBlock Number From Deed4.3Plat NamePLAT_NAMEText150Tax SystemPLAT_NAMEPlat Name From Deed4.16Homestead ExemptionHOMESTEADText10Tax SystemHOMESTEADHomesteadHomestead Status from CAMA - Yes or No4.17Acres (Polygon)ACRES_POLYDouble11 (2 decimal places)Parcel FabricCALC_ACREAGEAcres in tax parcel calculated from Shape Area.4.18Acres (Deed)ACRES_DEEDDouble11 (2 decimal places)Tax SystemACRES_DEEDFrom Deed4.19Estimated Value of LandEMV_LANDIntegerLongTax SystemEMV_LANDEstimated market value (EMV) is the value determined by the assessor as the price the property would likely sell for on the open market4.20Estimated Value of BuildingEMV_BLDGIntegerLongTax SystemEMV_BLDGEstimated market value (EMV) is the value determined by the assessor as the price the property would likely sell for on the open market4.21Estimated Value TotalEMV_TOTALIntegerLongTax SystemEMV_TOTALEstimated market value (EMV) is the value determined by the assessor as the price the property would likely sell for on the open market4.23Market YearMKT_YEARIntegerShortScriptAsmnt_YearYear Estimated Value is based.4.24Net Tax CapacityTAX_CAPACIntegerLongTax SystemNTCNet Tax Capacity (NTC) is the base value used in calculating most of a property's tax.4.25Total TaxTOTAL_TAXIntegerLongTax SystemTOTAL_TAXTax Information4.26Special AssessmentSPEC_ASSESIntegerLongTax SystemSPEC_ASSESTax InformationClay County Elements7.8Net TaxNET_TAXIntegerLongTax SystemNET_TAXTotal Tax - Special Assessments7.9Tax Payments MadeTAX_PAYMENTSIntegerLongTax SystemTAX_PAYMENTSTax Information7.10Tax Balance Due Current Tax YearTAX_BALANCE_DUEIntegerLongTax SystemTAX_BALANCE_DUETax Information7.11Tax Delinquent DateTAX_DELQ_DATEDate36Tax SystemTAX_DELQ_DATETax Information7.12Prior years Delinquent Tax DuePRIOR_YEAR_TAX_DELQ_DUEIntegerLongTax SystemPRIOR_YEAR_TAX_DELQ_DUETax InformationTax and Survey Elements4.27Use Classification 1USECLASS1Text100Tax SystemCLASSProperty Classification from CAMA (i.e. 200 Agricultural)4.45Year BuiltYEAR_BUILTIntegerShortTax SystemYEAR_BUILTFrom CAMA4.49Green Acres ProgramGREEN_ACREText10Tax SystemGREEN_ACREYesNoUnknownYes or No - Tax deferral program for agricultural land in areas where land values are affected by development or non-agricultural influences.4.51Agricultural PreserveAG_PRESERVText10Tax SystemAG_PRESERVYesNoUnknownYes or No - Reduction in property tax to enable agricultural use of land in areas facing development pressures.4.54Abbreviated Legal DescriptionABB_LEGALText254Tax SystemLEGAL_DESC_L1From DeedClay County Elements7.13Abbreviated Legal Description 2ABB_LEGAL_2Text254Tax SystemLEGAL_DESC_L2From DeedSitus Address Elements2.2Address NumberANUMBERIntegerLongTax SystemBLDG_NUMThe numeric identifier for a land parcel, house, building or other location along a thoroughfare or within a community.2.8Street NameST_NAMEText60Tax SystemSTREETNAMEStreet name that identifies the particular thoroughfare. (With ordinals: st, nd, rd, th.)2.9Street Name Post TypeST_POS_TYPText15Tax SystemSTREETTYPEStreetPostTypeA word or phrase that follows the street name and identifies a type of thoroughfare.2.10Street Name Post DirectionalST_POS_DIRText9Tax SystemSUFFIX_DIRStreetDirectionalA word following the Street Name that indicates the direction or position of the thoroughfare relative to an arbitrary starting point or line, or the sector where it is located.2.13Subaddress Identifier 1SUB_ID1Text30Tax SystemUNIT_INFOThe letters, numbers, words or combination thereof used to distinguish different subaddresses of the same type when several occur in the same feature.Area Elements3.3Postal Community NamePOSTCOMMText40Tax SystemCITYLUT_USPSPreferredCityCity name for the ZIP Code of the tax parcel. 3.6State CodeSTATE_CODEText2Tax SystemSTATEStateCodeLUT_StateThe two-character code of the state in which the tax parcel is physically located. Situs Address Elements2.16ZIP CodeZIPText5Tax SystemZIP_CODEThe 5-digit ZIP code for the address.Area Elements3.1CTU NameCTU_NAMEText100CTUNameLUT_CTU_CountyThe name of the city, township, or unorganized territory (CTU) in which the tax parcel is physically located. 3.2CTU CodeCTU_ID_TXTText8CTUIDTextLUT_CTU_CountyThe official Federal Geographic Names Information Systems unique identifier code for the city, township or unorganized territory in which the tax parcel is physically located.3.4County CodeCO_CODEText5Script"27027"CountyCodeLUT_CountyThe combination of the two-character state numeric code and the three-character county code is 270273.5County NameCO_NAMEText40Script"Clay"CountyNameLUT_CountyThe name of the county is Clay.Clay County Elements7.14Property TypePropertyTypeText25Parcel FabricPropertyTypeFrom Deed: Abstract or Torrens7.15Referendum Market ValueRMVDouble28 (2 decimal places)Tax SystemRMVReferendum market value (RMV) is the tax base for referendum levies. 7.16Taxable Market ValueTMVDouble28 (2 decimal places)Tax SystemTMVTaxable market value (TMV) refers to the amount of value that is used in calculating taxes for a property.

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE. The boundaries were digitized based on legal descriptions from the US Postal Service. The date of the legal descriptions is unknown. Zip code areas for which a legal description could not be obtained were digitized using the zip code contained in the site address from the August, 2009 Oakland County tax parcel feature class. The key attribute is Zip.

A. SUMMARY This dataset contains sales tax collected in San Francisco for calendar years 2015 through the quarter preceding the most recent one. Sales tax is aggregated, or summed, using current supervisor districts. See “How to use this dataset” below for more details on how the data has been aggregated. Sales tax is collected by businesses on many types of transactions and regulated by the California Department of Tax and Fee Administration.

B. HOW THE DATASET IS CREATED Data is collected by HDL. The data is then aggregated based on the criteria outlined in the "How to use this dataset" section.

C. UPDATE PROCESS This dataset will be updated quarterly.

D. HOW TO USE THIS DATASET This dataset can be used to analyze sales tax data over time across Supervisor Districts in San Francisco. Due to data privacy protection regulations for businesses, sales tax data is not available for all geographic boundary. For example, boundaries where there are less than 4 businesses paying sales tax or a single business that pays 80% or more of the total sales tax have been combined with neighboring geographic boundary to protect the confidentiality of affected businesses.

A map of of current supervisor districts can be found here.

E. RELATED DATASETS Sales Tax by Supervisor District Sales Tax by Census Block Sales Tax by Analysis Neighborhood Sales Tax by Zip Code

Exempt organization information is extracted monthly from the Internal Revenue Service’s Business Master File. This is a cumulative file, and the data are the most recent information the IRS has for these organizations. If you have any questions about the tax-exempt organizations or the content of the files, please contact TE/GE Customer Account Services toll-free line at 1-877-829-5500 State is determined from the filing address and generally represent the location of an organization’s headquarters, which may or may not represent the state(s) in which an organization has operations. Records are sorted by Employer Identification Number (EIN). This dataset is for Connecticut only. The IRS exempt organization data have been accumulated since the inception of the tax-exempt statutes. A determination letter is issued to an organization upon the granting of an exemption and is considered valid throughout the life of the organization, as long as the organization complies with the provisions of its exemption. If an organization's exemption is revoked, an announcement to inform potential donors of the revocation is published in the Internal Revenue Bulletin. In addition, the organization’s name is removed from publicly accessible venues, including this file. Updated nightly. NOTE: Split-interest trusts are not included in this database. FIELDS AVAILABLE All exempt organization records on this file will contain the following data fields: Column Name Contents EIN Employer Identification Number (EIN) NAME Primary Name of Organization ICO In Care of Name STREET Street Address CITY City STATE State ZIP Zip Code GROUP Group Exemption Number SUBSECTION Subsection Code AFFILIATION Affiliation Code CLASSIFICATION Classification Code(s) RULING Ruling Date DEDUCTIBILITY Deductibility Code FOUNDATION Foundation Code ACTIVITY Activity Codes ORGANIZATION Organization Code STATUS Exempt Organization Status Code TAX_PERIOD Tax Period ASSET_CD Asset Code INCOME_CD Income Code FILING_REQ_CD Filing Requirement Code PF_FILING_REQ_CD PF Filing Requirement Code ACCT_PD Accounting Period ASSET_AMT Asset Amount INCOME_AMT Income Amount (includes negative sign if amount is negative) REVENUE_AMT Form 990 Revenue Amount (includes negative sign if amount is negative) NTEE_CD National Taxonomy of Exempt Entities (NTEE) Code SORT_NAME Sort Name (Secondary Name Line)

This dataset contains all properties that are eligible for tax sale as of May 6, 2021. Note: properties may be removed from the sale daily and this dataset only represents a snapshot in time as of May 6. This dataset does not constitute an official copy of the list.The data include owner-occupied properties. On May 3rd, 2021, Mayor Scott announced that tax lien certificates on these properties would not be sold, however they are included in these data for reference. Use the field "BEING_REMO" to filter out properties that will no longer be sold based on Mayor Scott’s announcement on May 3.Change Log2022-04-14 Changed name of dataset from "Current Tax Sale List"Data DictionaryField Name DescriptionBLOCK The block number for the property.LOT The lot number for the property.OWNERSHIP INDICATOR Indicator for type of ownership on property. H = Owner occupied principal residence D = Dual use N Not owner occupiedLAND USE CODE The land use code for the parcel. R = residential C = commercial I = IndustrialOWNER NAME The name of the owner of the property.TAX BASE The value of the property.CITY TAX The annual city property tax based on the assesed value of the property.STATE TAX The annual state property tax based on the assesed value of the property.TOTAL TAXES The sum of the "City Tax" and "State Tax" columns.TOTAL 3 YEAR TAXES DUE The total remaining taxes owed for the property.TOTAL LIENS DUE The sum of the columns "Total 3 Year Taxes Due" and "Total Lien". This is the current total amount of money owed including liens and past due taxes.TOTAL LIEN The total amount of liens on the property.YEARS ELIGIBLE FOR SALE The number of years the property has been eligible for tax sale in the past.DEED DATE The date that ownership of the property was transferred to the owner.COUNCIL DISTRICT The city council district where the property is located.NEIGHBORHOOD The neighborhood where the property is located.WHEN SOLD The last time the tax lien certificate on the property was sold. Street Address The street number and street name of the property. City The city the property is in (Baltimore). State The state the property is in (Maryland). ZIP The ZIP code of the property. Latitude The latitude of the property. Longitude The longitude of the property. To leave feedback or ask a question about this dataset, please fill out the following form: 2021 Tax Sale List (With Exempted Properties) feedback form.

Abstract (en): This study of 767 adults in the Detroit metropolitan area provides information on their religious beliefs and practices, as well as their feelings about various forms of taxation such as sales tax, income tax, and property tax. The collection was a combination of two separate studies: THE VITALITY OF SUPERNATURAL EXPERIENCE by Guy Swanson, and A FISCAL RESEARCH PROGRAM by Harvey Brazer. Respondents were asked about their beliefs in the existence and characteristics of God, the amount of influence they felt that God had in their life, and how they thought God would feel about various situations. Also explored was the membership and level of activity in formal organizations for both the respondent and the respondent's spouse. The respondent was also asked to evaluate the performance of several institutions and professional groups such as colleges, their position on televisions in classrooms, the Federal Courts, doctors, and scientists. In addition, the respondent was asked to list the problems in the United States that were badly in need of resolution and to evaluate who was to blame for the problems and what could be done to solve them. Other items probed the respondent's opinions of educational television stations, the comparative quality of utility companies' services, government spending, and the most important things in life. Attitudes toward the use of taxes or use fees to pay for parks and garbage collection were also elicited. Demographic variables specify age, sex, race, education, place of birth, marital status, occupation, length of residence in the Detroit area, home ownership, length of time at present residence, number of children, original nationality of husband's and wife's family, political affiliation, and amount and sources of income. More information about the Detroit Area Studies Project is available on this Web site. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. A multistage sample of 767 adults aged 21 and older in households in the Detroit metropolitan area in 1959. 2010-09-30 SAS, SPSS, and Stata ready-to-go files were added. Unknown Codes: The following variables show frequencies for values without explicit labels and were therefore labeled as unknown: V465, V467, V468, V469, V482, V488, V489Documentation Note: V60 "Variable exists on the archives tape but is not recorded in the codebook. Values range 0-7".

This dataset contain information from IRS 990 series forms for 2020. Tax-exempt organizations, nonexempt charitable trusts, and section 527 political organizations file Form 990 to provide the IRS with the information required by section 6033.

Column description

• ein : Employer Identification Number
• business_name_control : Business name
• business_name_ln1 : Business name line 1
• business_name_ln2 : Business name line 2
• zip_code: ZIP code
• address : Address
• city : City
• state : State abbreviation code
• principal_officer : Name of Principal Officer
• gross_receipts : Gross receipts
• 501c3_org : Indicates a 501(c)(3) organization
• website : Website address
• website_ez : Website address in 990 EZ Form
• org_type_corporation : Type of organization - corporation
• org_type_corporation_ez : Type of organization - corporation in 990 EZ Form
• org_type_trust : Type of organization - trust
• org_type_trust_ez : Type of organization - trust in 990 EZ Form
• org_type_association : Type of organization - association
• org_type_association_ez : Type of organization - association in 990 EZ Form
• org_type_other : Type of organization - other
• org_type_other_ez : Type of organization - other in 990 EZ Form
• org_type_other_description : Type of organization - other, description
• formation_year : Formation year
• legal_domicile_state : Legal domicile state
• activity_or_mission_description : Activity or mission description
• num_voting_members_governing_body : Number of voting members of the governing body
• num_independent_voting_members : Number of independent voting members of the governing body
• total_num_employees : Total number of individuals employed in calendar year 2020
• total_num_volunteers : Total number of volunteers
• total_unrelated_business_income : Total unrelated business income
• net_unrelated_business_taxable_income : Net unrelated business taxable income
• contribution_grants_py : Contributions and grants (revenue) - prior year
• contribution_grants_cy : Contributions and grants (revenue) - current year
• program_service_revenue_py : Program service revenue - prior year
• program_service_revenue_cy : Program service revenue - current year
• investment_income_py : Investment Income Amount - prior year
• investment_income_cy : Investment Income Amount - current year
• other_revenue_py : Other Revenue Amount - prior year
• other_revenue_cy : Other Revenue Amount - current year
• total_revenue_py : Total Revenue Amount - prior year
• total_revenue_cy : Total Revenue Amount - current year
• grants_and_similar_amounts_paid_py : Grants and similar amounts - prior year
• grants_and_similar_amounts_paid_cy : Grants and similar amounts - current year
• benefits_paid_to_members_py : Benefits paid to members - prior year
• benefits_paid_to_members_cy : Benefits paid to members - current year
• salaries_compensations_emp_benefits_paid_py : Salaries, compensations, employee benefits paid prior year
• salaries_compensations_emp_benefits_paid_cy : Salaries, compensations, employee benefits paid current year
• total_professional_fundraising_expense_py : Total professional fundraising expense - prior year
• total_professional_fundraising_expense_cy : Total professional fundraising expense - current year
• total_fundrasing_expense_cy : Description: Total fundraising expense - current year
• other_expenses_py : Other expenses - prior year
• other_expenses_cy : Other expenses - current year
• total_expenses_py : Total Expenses - prior year
• total_expenses_cy : Total Expenses - current year
• revenue_less_expenses_py : Revenues less expenses - prior year
• revenue_less_expenses_cy : Revenues less expenses - current year
• total_assests_boy : Total Assets, Beginning of the year amount
• total_assests_eoy : Total Assets, End of the year amount
• total_liabilities_boy : Total Liabilities, Beginning of the year amount
• total_liabilities_eoy : Total Liabilities, End of the year amount
• net_assets_fund_balances_boy : Net Assets or Fund Balances, Beginning of the year amount
• net_assets_fund_balances_eoy : Net Assets or Fund Balances, End of the year amount
• mission_description : Mission Description
• significant_new_program_services : Did the organization undertake any significant program services during the year which were not listed on the prior Form 990 or 990-EZ?
• significant_change_program_services : Did the organization cease conducting, or make significant changes in how it conducts, any program services?
• program_expense_amount : Program Expenses Amount
• program_grant_amount : Program Grants Amount
• program_revenue_amount : Program Revenue Amount
• other_program_services_description : Other program services description
• total_program_service_expenses : ...

Pursuant to Section 7-100l of the Connecticut General Statutes, each municipality is required to transmit a digital parcel file and an accompanying assessor’s database file (known as a CAMA report), to its respective regional council of governments (COG) by May 1 annually. The dataset has combined the Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2025 into a single dataset. This dataset is designed to make it easier for stakeholders and the GIS community to use and access the information as a geospatial dataset. Included in this dataset are geometries for all 169 municipalities and attribution from the CAMA data for all but one municipality. These data were gathered from the CT municipalities by the COGs and then submitted to CT OPM. This dataset was created on September 2025 from data collected in 2024-2025. Data was processed using Python scripts and ArcGIS Pro for standardization and integration of the data. To learn more about Parcel and CAMA in CT visit our Parcels Page in the Geodata Portal.Coordinate system: This dataset is provided in NAD 83 Connecticut State Plane (2011) (EPSG 2234) projection as it was for 2024. Prior versions were provided at WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857). Ownership Suppression: The updated dataset includes parcel data for all towns across the state, with some towns featuring fully suppressed ownership information. In these instances, the owner’s name was replaced with the label "Current Owner," the co-owner’s name will be listed as "Current Co-Owner," and the mailing address will appear as the property address itself. For towns with fully suppressed ownership data, please note that no "Suppression" field was included in the submission to confirm these details and this labeling approach was implemented as the solution.New Data Fields:The new dataset introduces the “Property Zip” and “Mailing Zip” fields, which will display the zip codes for the owner and property.Service URL:In 2024, we implemented a stable URL to maintain public access to the most up-to-date data layer. Users are strongly encouraged to transition to the new service as soon as possible to ensure uninterrupted workflows. This URL will remain persistent, providing long-term stability for your applications and integrations. Once you’ve transitioned to the new service, no further URL changes will be necessary.CAMA Notes:The CAMA underwent several steps to standardize and consolidate the information. Python scripts were used to concatenate fields and create a unique identifier for each entry. The resulting dataset contains 1,354,720 entries and information on property assessments and other relevant attributes.CAMA was provided by the towns.Spatial Data Notes:Data processing involved merging the parcels from different municipalities using ArcGIS Pro and Python. The resulting dataset contains 1,282,833 parcels.No alteration has been made to the spatial geometry of the data.Fields that are associated with CAMA data were provided by towns.The data fields that have information from the CAMA were sourced from the towns’ CAMA data.If no field for the parcels was provided for linking back to the CAMA by the town a new field within the original data was selected if it had a match rate above 50%, that joined back to the CAMA.Linking fields were renamed to "Link".All linking fields had a census town code added to the beginning of the value to create a unique identifier per town.Any field that was not town name, Location, Editor, Edit Date, or a field associated back to the CAMA, was not used in the creation of this Dataset.Only the fields related to town name, location, editor, edit date, and link fields associated with the towns’ CAMA were included in the creation of this dataset. Any other field provided in the original data was deleted or not used.Field names for town (Muni, Municipality) were renamed to "Town Name".Attributes included in the data: Town Name OwnerCo-OwnerLinkEditorEdit DateCollection year – year the parcels were submittedLocationProperty ZipMailing AddressMailing CityMailing StateMailing ZipAssessed TotalAssessed LandAssessed BuildingPre-Year Assessed Total Appraised LandAppraised BuildingAppraised OutbuildingConditionModelValuationZoneState UseState Use DescriptionLand Acre Living AreaEffective AreaTotal roomsNumber of bedroomsNumber of BathsNumber of Half-BathsSale PriceSale DateQualifiedOccupancyPrior Sale PricePrior Sale DatePrior Book and PagePlanning RegionFIPS Code *Please note that not all parcels have a link to a CAMA entry.*If any discrepancies are discovered within the data, whether pertaining to geographical inaccuracies or attribute inaccuracy, please directly contact the respective municipalities to request any necessary amendmentsAdditional information about the specifics of data availability and compliance will be coming soon.If you need a WFS service for use in specific applications : Please Click HereContact: opm.giso@ct.gov