This Africa Geocoding locator is a view of the World Geocoding Service constrained to search for places in the countries of Africa. The World Geocoding Service finds addresses and places in all supported countries around the world in a single geocoding service. The service can find point locations of addresses, cities, landmarks, business names, and other places. The output points can be visualized on a map, inserted as stops for a route, or loaded as input for a spatial analysis.The service is available as both a geosearch and geocoding service:Geosearch Services – The primary purpose of geosearch services is to locate a feature or point of interest and then have the map zoom to that location. The result might be displayed on the map, but the result is not stored in any way for later use. Requests of this type do not require a subscription or a credit fee. Geocoding Services – The primary purpose of geocoding services is to convert an address to an x,y coordinate and append the result to an existing record in a database. Mapping is not always involved, but placing the results on a map may be part of a workflow. Batch geocoding falls into this category. Geocoding requires a subscription. An ArcGIS Online subscription will provide you access to the World Geocoding service for batch geocoding.The service can be used to find address and places for many countries around the world. For detailed information on this service, including a data coverage map, visit the World Geocoding service documentation.

Vermont composite geocoding service built with VT E911 data. This service can be used by ArcGIS Pro 2.8.x+ to batch geocode addresses stored in a table. It also can be used as a geocoder with most ArcGIS Online apps, as well as QGIS. [How To Use The Vermont Geocoding Service]This ArcGIS Online item utilizes the ArcGIS Server geocoding service at this REST Endpoint: https://maps.vcgi.vermont.gov/arcgis/rest/services/EGC_services/GCS_E911_COMPOSITE_SP_v2/GeocodeServer

The World Geocoding Service finds addresses and places in all supported countries around the world in a single geocoding service. The service can find point locations of addresses, cities, landmarks, business names, and other places. The output points can be visualized on a map, inserted as stops for a route, or loaded as input for a spatial analysis.The service is available as both a geosearch and geocoding service:Geosearch Services – The primary purpose of geosearch services is to locate a feature or point of interest and then have the map zoom to that location. The
result might be displayed on the map, but the result is not stored in
any way for later use. Requests of this type do not require a subscription or a credit fee. Geocoding Services – The primary purpose of geocoding services is to convert an address to an x,y coordinate and append the result to an existing record in a
database. Mapping is not always involved, but placing the results on a
map may be part of a workflow. Batch geocoding falls into this
category. Geocoding requires a subscription. An ArcGIS Online subscription will provide you access to the World Geocoding service for batch geocoding.The service can be used to find address and places for many countries around the world. For detailed information on this service, including a data coverage map, visit the World Geocoding service documentation.

VT E911 Composite geocoder - uses ESITE, RDSNAME, and RDSRANGE. REFRESHED WEEKLY. VCGI, in collaboration with the VT E911 Board, has created a suite of geocoding services that can be used to batch geocode addresses using ArcGIS Desktop 10.x. This service can also be integrated into ESRI ArcGIS web-based mapping applications.Input Address Requirements Must use valid E911 addresses (street style addressing...no P.O. box addresses!) and E911 town names. Limitations Don't attempt to geocode more than 50000 records or so. You must have an Internet connection to use the services. A DSL, cable, or other high bandwidth connection is the best option. Addresses other than E911 addresses are not supported. ArcGIS Pro - How To:Startup ArcGIS ProUnder the "Insert" ribbon select Connections --> New ArcGIS Server. Server URL = https://maps.vcgi.vermont.gov/arcgis/servicesBrowse to the ./EGC_services folder and select GEOCODE_COMPOSITE (or GEOCODE_ESITE).Add the table you want to geocode to project, then right-click and select "Geocode Table". Choose the “Go to Tool” option at the bottom of the dialogue box.Make selections and run geocoder.ArcGIS Desktop (ArcMap) - How To: Startup ArcMap 10+ Add a table containing VT addresses to geocode. ?Click the "Add Data" button.Navigate to your table, choose to add your tableRight-click on the table in the table of contentsSelect "Geocode Addresses...".Select "Add" in the dialog box.Browse to the "GIS Servers" icon in your catalog, then double click "Add ArcGIS Server".Select "Use GIS Services", then Next.ServerURL = https://maps.vcgi.vermont.gov/arcgis/services then click finish.Browse to "arcgis on maps.vcgi.org (user)". Browse to .\EGC_services folder.Select GECODE_ESITE (or GEOCODE_COMPOSITE). Click OK.Select whatever options you want in the geocode dialog box, including output, then click ok.The output will be automatically added to your ArcMap session.

The ArcGIS World Geocoding Service finds addresses and places in all supported countries around the world in a single geocoding service. The service can find point locations of addresses, cities, landmarks, business names, and other places. The output points can be visualized on a map, inserted as stops for a route, or loaded as input for a spatial analysis.The service is available as both a geosearch and geocoding service:Geosearch Services – The primary purpose of geosearch services is to locate a feature or point of interest and then have the map zoom to that location. The result might be displayed on the map, but the result is not stored in any way for later use. Requests of this type do not require a subscription or a credit fee. Geocoding Services – The primary purpose of geocoding services is to convert an address to an x,y coordinate and append the result to an existing record in a database. Mapping is not always involved, but placing the results on a map may be part of a workflow. Batch geocoding falls into this category. Geocoding requires a subscription. An ArcGIS Online Subscription, or ArcGIS Location Platform Subscription, will provide you access to the ArcGIS World Geocoding service for batch geocoding.The service can be used to find address and places for many countries around the world. For detailed information on this service, including a data coverage map, visit the ArcGIS World Geocoding service documentation.

Context

This dataset contains the locations found in the Kiva datasets included in an administrative or geographical region. You can also find poverty data about this region. This facilitates answering some of the tough questions about a region's poverty.

Content

In the interest of preserving the original names and spelling for the locations/countries/regions all the data is in Excel format and has no preview (I think only the Kaggle recommended file types have preview - if anyone can show me how to do this for an xlsx file, it will be greatly appreciated)

The Tables datasets contain the most recent analysis of the MPI on countries and regions. These datasets are updated regularly. In unique regions_names_from_google_api you will find 3 levels of inclusion for every geocode provided in Kiva datasets. (village/town, administrative region, sub-national region - which can be administrative or geographical). These are the results from the Google API Geocoding process.

Files:

• all_kiva_loans.csv

Dropped multiple columns, kept all the rows from loans.csv with names, tags, descriptions and got a csv file of 390MB instead of 2.13 GB. Basically is a simplified version of loans.csv (originally included in the analysis by beluga)

• country_stats.csv

1. population source: https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)
2. population_below_poverty_line: Percentage
3. hdi: Human Development Index
4. life_expectancy: Life expectancy at birth
5. expected_years_of_schooling: Expected years of schooling
6. mean_years_of_schooling: Mean years of schooling
7. gni: Gross national income (GNI) per capita This dataset was originally created by beluga.

• all_loan_theme_merged_with_geo_mpi_regions.xlsx

This is the loan_themes_by_region left joined with Tables_5.3_Contribution_of_Deprivations. (all the original entries from loan_themes and only the entries that match from Tables_5; for the regions that lack MPI data, you will find Nan)

These are the columns in the database:

1. Partner ID
2. Field Partner
3. Name
4. sector
5. Loan Theme ID
6. Loan Theme Type
7. Country
8. forkiva
9. number
10. amount
11. geo
12. rural_pct
13. City
14. Administrative region
15. Sub-national region
16. ISO
17. World region
18. Population Share of the Region (%)
19. region MPI
20. Education (%)
21. Health (%)
22. Living standards (%)
23. Schooling (%)
24. Child school attendance (%)
25. Child Mortality (%)
26. Nutrition (%)
27. Electricity (%)
28. Improved sanitation (%)
29. Drinking water (%)
30. Floor (%)
31. Cooking fuel (%)
32. Asset ownership (%)

• mpi_on_regions.xlsx

Matched the loans in loan_themes_by_region with the regions that have info regarding MPI. This dataset brings together the amount invested in a region and the biggest problems the said region has to deal with. It is a join between the loan_themes_by_region provided by Kiva and Tables 5.3 Contribution_of_Deprivations.

It is a subset of the all_loan_theme_merged_with_geo_mpi_regions.xlsx, which contains only the entries that I could match with poverty decomposition data. It has the same columns.

• Tables_5_SubNational_Decomposition_MPI_2017-18.xlsx

Multidimensional poverty index decomposition for over 1000 regions part of 79 countries.

Table 5.3: Contribution of deprivations to the MPI, by sub-national regions
This table shows which dimensions and indicators contribute most to a region's MPI, which is useful for understanding the major source(s) of deprivation in a sub-national region.

Source: http://ophi.org.uk/multidimensional-poverty-index/global-mpi-2016/

• Tables_7_MPI_estimations_country_levels.xlsx

MPI decomposition for 120 countries.

Table 7 All Published MPI Results since 2010
The table presents an archive of all MPI estimations published over the past 5 years, together with MPI, H, A and censored headcount ratios. For comparisons over time please use Table 6, which is strictly harmonised. The full set of data tables for each year published (Column A), is found on the 'data tables' page under 'Archive'.

The data in this file is shown in interactive plots on Oxford Poverty and Human Development Initiative website. http://www.dataforall.org/dashboard/ophi/index.php/

• unique_regions_from_kiva_loan_themes.xlsx

These are all the regions corresponding to the geocodes found in Kiva's loan_themes_by_region. There are 718 unique entries, that you can join with any database from Kiva that has either a coordinates or region column.
Columns:

• geo: pair of Lat, Lon (from loan_themes_by_region)

• City: name of the city (has the most NaN's)

• Administrative region: first level of administrative inclusion for the city/location; (the equivalent of county for US)

• Sub-national region: second level of administrative inclusion for the geo pair. (like state for US)

• Country: name of the country

Acknowledgements

Thanks to Shane Lynn for the batch geocoding and to Joseph Deferio for reverse geocoding:

https://www.shanelynn.ie/batch-geocoding-in-python-with-google-geocoding-api/

https://github.com/jdeferio/Reverse_Geocode

The MPI datasets you can find on the Oxford website (http://ophi.org.uk/) under Research.

"Citation: Alkire, S. and Kanagaratnam, U. (2018)

“Multidimensional Poverty Index Winter 2017-18: Brief methodological note and results.” Oxford Poverty and Human Development Initiative, University of Oxford, OPHI Methodological Notes 45."

Data from European cities with results of test for quality addresses data algorithm (paper ISPS IJGI).Addresses data used on this paper are available on these websites:A) OpenAddresses: https://batch.openaddresses.io/dataB) OpenStreetMap: https://wiki.openstreetmap.org/wiki/Downloading_dataC) Google Places: https://developers.google.com/maps/documentation/places/web-service/overview?D) Bing: https://learn.microsoft.com/en-us/bingmaps/rest-services/locations/E) Here: https://developer.here.com/documentation/geocoding-search-api/*NOTES: Due to rights and property reasons, we can not distribute commercial and authoritative addresses data used on this study

The dataset contains locations and attributes of building construction and alteration permits applied for and approved by the District of Columbia Department of Buildings. These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC.

The dataset contains locations and attributes for above ground permits applied for and approved by the District Department of Transportation. They are existing occupied constructions and events. Examples include: moving trucks, roll off debris container, moving storage container, construction staging area, mobile crane work zone, other reserved parking. The public space permit process is described on the DDOT website https://ddot.dc.gov.These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.

ABSTRACT Objectives: to analyze the spatial-temporal distribution of leprosy in a priority municipality for leprosy control. Methods: ecological study, conducted in a city in the Northeast of Brazil, whose analysis units were census sectors. The study used compulsory notification data for cases registered between 2008 and 2017. TerraView software and the Batch Geocode tool was used for geocoding. The detection of spatial-temporal agglomerations of high relative risks was done by scanning statistics. Results: the spatial-temporal distribution of cases was heterogeneous, creating four agglomerations of high relative risks in the urban area of the municipality between the years 2008 and 2012; and annual prevalence rates classified from high to hyperendemic. Conclusions: areas of higher risk and concentration of the disease in space-time were linked to the characteristics of high population density and social vulnerability of these spaces, raising the prioritization of health professionals’ actions, systems, and services for control, and monitoring the disease.

The dataset contains locations and attributes for above ground permits applied for and approved by the District Department of Transportation. They are existing occupied constructions and events. Examples include: moving trucks, roll off debris container, moving storage container, construction staging area, mobile crane work zone, other reserved parking. The public space permit process is described on the DDOT website https://ddot.dc.gov.These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.

Location of TxDot properties generated from TxDot files to point layer files batch geocoding.

Street_NoNum_and_ZipCode_Composite uses the NYS Streets and Zipcode boundaries to return a match within the street segment. Any address that does not successfully geocode to the first composite can then be run through the second composite locator (Street_NoNum_and_ZipCode_Composite). Recognizing that hits from this locator will not be spatially accurate. This composite locator is made up of the following locators.Locator NameSource DataDescription4A_SS_NoNum_ZipNameNYS Street SegmentsNYS Street Segments dataset using the postal zip code name for the City name in the locator. The location is placed on a street segment with the matching name. Please note this may or may not be the correct street segment.4B_SS_NoNum_CTNameNYS Street SegmentsNYS Street Segments dataset using the city or town name is used for the city name in the locator. The location is placed on a street segment with the matching name. Please note this may or may not be the correct street segment.4C_SS_NoNum_PlaceNameNYS Street SegmentsNYS Street Segments dataset using the alternate place name is used for the city name in the locator. This field is populated using the NYS Villages and Indian Reservations, the Census Designated Places and Alternate Acceptable Zip Code Names from the USPS. These areas do not exist everywhere so there will be a limited number of segments with this attribute. The location is placed on a street segment with the matching name. Please note this may or may not be the correct street segment.5_ZipCodePtsZip Code boundariesPoint placed at the centroid of the Zip Code boundaries. Currently, the geocoding service will return all of the results when using the Find Tool within ArcGIS. The user will then be responsible for choosing which of the results they want to keep. The SAM Address Points are the most accurate data available and should be picked anytime a result is returned from one of the SAM address point locators. If the geocoding service is used in the ESRI batch tool, the locator will return a Match from the first locator it comes to in the cascading order. If there are multiple locators with the same score or within the same locator the first result is returned and it is coded as a Tie.The locators will output a field named 'User_fld' which should be used in conjunction with the Loc_Name field. When the Loc_Name field contains one of the Address Point locators (1A, 1B or 1C) this field will contain either a 1,2,3,4 or a 5. When the Loc_Name field contains anything other than the Address Point locators, the 'User_fld' will either be NULL or "0". The numeric values correspond with the type of Address Point that was located:RooftopPrimary Structure EntranceDrivewayParcel CentroidMiscellaneous

The dataset contains locations and attributes of building construction and alteration permits applied for and approved by the District of Columbia Department of Buildings. These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC.

The dataset contains locations and attributes for above ground permits applied for and approved by the District Department of Transportation. They are existing occupied constructions and events. Examples include: moving trucks, roll off debris container, moving storage container, construction staging area, mobile crane work zone, other reserved parking. The public space permit process is described on the DDOT website https://ddot.dc.gov.These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.

The dataset is continuously updated and contains locations and attributes of building construction and alteration permits applied for and approved by the District of Columbia Department of Consumer and Regulatory Affairs. The building permit process is described on the DCRA website https://dcra.dc.gov/These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.

The data demonstrates the location of CDBG-DR-funded buyout activities as part of the Office of Community Planning and Development's (CPD) Disaster Recovery Buyout Program.The data is derived from an extract of HUD CPD’s Disaster Recovery Grants Reporting (DRGR) System, an address-level dataset that includes Community Development Block Grant – Disaster Recovery activities for certain grantees and over a limited span of time during which grantees were required to report addresses of certain funded activities. Buyouts are a unique disaster-related activity made eligible through a waiver in the allocation of CDBG-DR grants following a natural hazard disaster. Under the waiver, grantees are permitted to use CDBG-DR funds to pay the pre-disaster or post-disaster value to acquire properties impacted by a natural hazard, usually flooding, for the purpose of risk reduction. The offer creates an incentive for impacted homeowners to relocate to a residence outside of a high hazard risk area. The property must be maintained by the local jurisdiction as open space indefinitely to eliminate future disaster liability. Each observation in the address-level dataset is a standardized, geocoded address at which a residential buyout took place. The buyouts were reported by grantees through March 31, 2020. The data extract was drawn, geocoded, processed, and aggregated to the census tract-level following the close of 2020 Q1. Only addresses that were geocoded to a moderate to high level of accuracy were included (LVL2KX = "R" (rooftop) or "4" (Zip+4 centroid)). The addresses extracted from DRGR were geocoded using the HUD Batch Geocoder which matches geocoordinates with standard Census geographies. The data contains buyouts completed through March 31, 2020. An activity is reported as “completed” once an end-use is met; for example, buyouts are complete upon legal acquisition of a property. All activities are aggregated to the 2010 Decennial Census Tract geography. Note: The data are not a comprehensive record of all buyouts funded with CDBG-DR. The activities were completed between October 2009 and March 2020. Grantees were required to enter addresses for these activities beginning in 2015. Early reporting of the address information is voluntary.The data being displayed are census tract level counts of CDBG-DR-assisted addresses. In order to protect privacy, census tracts where there were fewer than 11 buyouts display a value of -4.To learn more about the Disaster Recovery Buyout Program, please visit: https://www.hudexchange.info/programs/cdbg-dr/disaster-recovery-buyout-program/#buyout-program-overview-considerations-and-strategies, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_HUD CPD CDBG-DR BuyoutsDate of Coverage: Cumulative through 2020 Q1

The dataset contains locations and attributes for above ground permits applied for and approved by the District Department of Transportation. They are existing occupied constructions and events. Examples include: moving trucks, roll off debris container, moving storage container, construction staging area, mobile crane work zone, other reserved parking. The public space permit process is described on the DDOT website https://ddot.dc.gov.These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.

Description: All current, active business licensesProcess: Data is queried from TRMS, processed to clean up common addressing issues (change "FT LOWELL" to "FORT LOWELL" - street name "1" become "1ST", funny "ÿ" characters are removed etc) and geocoded. Geocoding is a 3 step process: 1 - whole table is geocoded to adparcel with a 9.3.1 geocoder (scores of 69 and higher become seperate layer)2 - anything with a score of 68 or below is joined to adparcel based on street #, street direction and street name - those that have a match have their address recalculated based on the full address in adparcel and are geocoded again with the 9.3.1 geocoder (become seperate layer)3 - those from step 2 that do not have a match in adparcel are goecoded to stnetall and everything with a score of 80 or above are exported out as a seperate layerLayers from steps 2 and 3 are appended into layer in step 1 and the original buslic FC has records deleted and the new data appended.Attributes: ACC_NUM: account numberACC_NAME: account nameOWN_CODE: ownership codeOWN_DESC: ownership descriptionTYPE_CODE: business type code (RRP is RENTAL REAL PROPERTY is a license for renting a building)TYPE_DESC: business type descriptionNAIC_CODE: NORTH AMERICAN INDUSTRY CLASSIFICATION SYSTEM number codesNAIC_DESC: NAICS descriptionLIC_TYPE: SVL (Service License - professional and service businesses, may collect use tax) or PLL (Privilige LicenseLicense, businesses that collect sales tax)DT_START: start date of businessDT_ISSUED: issued date of current business license CITY: Tucson or several astericks with numbers in between indicates multiple addressesSTATE: AZZIP_CODE: zipcode from TRMSADD_ORIG: original address from TRMS, including suite/unit numberADD_MATCH: address account matched to (this will not necessary match the ADD_ORIG field)MT: match type, number indicates which geocoding step was used (1 = highest quality match, 3 = lowest quality match)---------------------------------------------------------------------------------------------------------------------------------------------------------------Who: Original TRMS (TAX MANTRA) data query and geoprocessing done by Johanna Kraus - March 2011Maintinance/Update Schedule: Data and geoprocessing are a batch job done weekly.-----------------------------------------------------------------------------------------------------------------------------------------------------------------Known Errors:All locations are approximate. Most data is mapped to existing address points (which are not precise locations),those records that don't have an equivilant address match are approximated along the address range of street. SeeMT field for more details.Fields for NAICS codes, business types and owner types may be missing data or incorrect.Data layer should not be considered a complete listing of all active businesses in Tucson. Please send spatial errors to GIS_IT@tucsonaz.govPlease send data errors to Tax-License@tucsonaz.gov

The dataset contains locations and attributes of above ground permits applied for and approved by the District Department of Transportation. They are newly occupied constructions and events. Examples include: moving truck, roll off debris container, moving storage container, construction staging area, mobile crane work zone, other reserved parking. The public space permit process is described on the DDOT website https://ddot.dc.gov.These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC. Use the MAR Geocoder to help complete this.