For many countries lat/lng are determined with an algorithm that searches the place names in the main geonames database using administrative divisions and numerical vicinity of the postal codes as factors in the disambiguation of place names. For postal codes and place name for which no corresponding toponym in the main geonames database could be found an average lat/lng of 'neighbouring' postal codes is calculated. Please let us know if you find any errors in the data set. ThanksFor Canada we have only the first letters of the full postal codes (for copyright reasons)For Ireland we have only the first letters of the full postal codes (for copyright reasons)For Malta we have only the first letters of the full postal codes (for copyright reasons)The Argentina data file contains 4-digit postal codes which were replaced with a new system in 1999.For Brazil only major postal codes are available (only the codes ending with -000 and the major code per municipality).For India the lat/lng accuracy is not yet comparable to other countries.Update frequency: 1 month

Our zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Comprehensive database of Canadian postal codes covering all provinces and territories

Now includes bridge to the 2016 census.

A self-hosted location dataset containing all administrative divisions, cities, and zip codes for Canada. All geospatial data is updated weekly to maintain the highest data quality, including coverage of complex regions within the country.

Use cases for the Global Zip Code Database (Geospatial data) - Address capture and validation - Map and visualization - Reporting and Business Intelligence (BI) - Master Data Management - Logistics and Supply Chain Management - Sales and Marketing

Data export methodology Our location data packages are offered in variable formats, including .csv. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features - Fully and accurately geocoded - Administrative areas with a level range of 0-4 - Multi-language support including address names in local and foreign languages - Comprehensive city definitions across countries

For additional insights, you can combine the map data with: - UNLOCODE and IATA codes - Time zones and Daylight Saving Times

Why do companies choose our location databases - Enterprise-grade service - Reduce integration time and cost by 30% - Weekly updates for the highest quality

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

An R script to extract and parse the Postal Code Conversion File (PCCF) from Statistics Canada/Canada Post.

The most complete and up-to-date Canadian postal code database for accurate customer profiling, direct marketing optimization, precise territory management, and serviceability insights. Postal Code Suite offers six digit postal code polygons so you can visualize postal code boundaries on a map. This package comes as a Canada-wide Esri File Geodatabase, including Multiple Enhanced Postal Codes, Forward Sortation Areas, Local Delivery Units and topographic layers, for the province/territory, as well as Canada wide layers for area codes, capital cities, provincial boundaries, municipal boundaries, topographic boundaries, time zones and water layers.

The Postal Code Conversion File (PCCF) is a digital file which provides the correspondence between the six character code and Statistics Canada's standard geographical areas (e.g. Census divisions, Census subdivisions, Federal Electoral Districts) for which census data and other statistics are produced. To obtain the postal code conversion file or for questions, consult the DLI contact at your educational institution. The current version of the PCCF links over 787,000 postal code records, created up to the end of July 1996, to the geographical areas used in the 1991 Census and to Universal Transverse Mercator System (UTM) coordinates and latitude/longitude coordinates. This new version contains a new field called the single postal code indicator. This field will be useful in cases where a given postal code is assigned to multiple standard geographic areas. It indicates which of the standard geographic units is the most representative of the postal code. The purpose of the PCCF is to provide linkage capabilities that can be used for numerous applications, such as market research, demographic studies and geocoding applications. The file allows users to cross-reference geographic coordinates, census areas, and user-defined areas. For example, one of its key strengths lied in its capacity to integrate census data with user data. For more information on this product or some of its applications, please refer to the 'Products and Services Manual' or contact the Regional Geographer at one of our Regional Reference Centres across Canada. During the 1970s, there was an increasing demand for a large variety of statistics for small areas. To aggregate data by geographic areas, different types of address elements were examined manually, or by computer, in order to properly assign a geographical code. This assignment was complicated by the great variety of address formats on data files and spelling variations in street names. The introduction of the postal code in the mid-1970s has led to an entirely new approach. The postal code could be used as a structured representation of a range of mailing addresses. If the postal codes were matched to a standard geographic unit once, and the results retained in a lookup table, then the complex task of structuring and matching addresses could be avoided.

Point data with complete civic address attributes that do not include postal code. Street address, civic number and suffix, PID number, community and county name, latitude and longitude coordinates, a unique civic ID for each point and other attributes are included.

This map shows U.S. zip code centroids and Canada postal code centroids of watercraft owners' home residences who passed through Idaho Watercraft Inspection Stations during the 2023 inspection season. Each centroid point is classified by color to represent the number of these watercraft owners who reside in that particular zip code or postal code. Data was aggregated from individual inspections entered into the 2023 Idaho Watercraft Inspection survey database. These watercraft inspections mainly concentrate on finding Invasive Quagga/Zebra mussels on and in watercraft based on visual inspection and last waterbody visited. Station inspectors also look for aquatic noxious weeds that might be attached to watercraft.

The Canadian Active Living Environments (Can-ALE) database is a geographic-based set of measures that represents the active living friendliness of Canadian communities. The primary envisioned use for Can-ALE is research and analysis of the relationship between the way communities are built and the physical activity levels of Canadians. Each of the measures was selected from fourteen potential measures identified by a literature review. Several considerations were weighed in deriving the Canada-wide set of measures, including: (1) the suitability of each measure across different Canadian regions and built areas (e.g., urban, suburban, rural areas); (2) the incorporation of high-quality, open and free-to-use data sources; and (3) the strength of the association between the derived measures with walking rates and active transportation (i.e., walking, cycling, and public transit use). Public transit use is included in the definition of active transportation, as public transit is shown to generate physical activity via walking to and from transit stops.The Can-ALE data were developed by Dr. Nancy Ross, Thomas Herrmann and William Gleckner, with funding from the Public Health Agency of Canada. Can-ALE measures have been developed for 2006 and 2016 census dissemination areas. Users are discouraged from performing longitudinal analyses using data from both the 2006 and 2016 datasets, as the derivation methodologies and census geographies changed between the reference years. ArcGIS was used by CANUE staff to associate the single link DMTI Spatial postal codes to the Statistics Canada dissemination areas boundary files, and then join the Access to Employment data to the postal codes, using dissemination area unique identifiers. There may be many postal codes within a single dissemination area - these will have the same index values and may not be suitable for summation, etc. Please refer to the Supporting Documentation.

Point data with complete civic address attributes that do not include postal code. Street address, civic number and suffix, PID number, community and county name, latitude and longitude coordinates, a unique civic ID for each point and other attributes are included.

Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication.Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication. You can see the map on Ottawa Public Health's website.Accuracy: Points of consideration for interpretation of the data:Data extracted by Ottawa Public Health at 2pm from the COVID-19 Ottawa Database (The COD) on May 12th, 2020. The COD is a dynamic disease reporting system that allow for continuous updates of case information. These data are a snapshot in time, reflect the most accurate information that OPH has at the time of reporting, and the numbers may differ from other sources. Cases are assigned to Ward geography based on their postal code and Statistics’ Canada’s enhanced postal code conversion file (PCCF+) released in January 2020. Most postal codes have multiple geographic coordinates linked to them. Thus, when available, postal codes were attributed to a XY coordinates based on the Single Link Identifier provided by Statistics’ Canada’s PCCF+. Otherwise, postal codes that fall within the municipal boundaries but whose SLI doesn’t, were attributed to the first XY coordinates within Ottawa listed in the PCCF+. For this reason, results for rural areas should be interpreted with caution as attribution to XY coordinates is less likely to be based on an SLI and rural postal codes typically encompass a much greater surface area than urban postal codes (e.i. greater variability in geographic attribution, less precision in geographic attribution). Population estimates are based on the 2016 Census. Rates calculated from very low case numbers are unstable and should be interpreted with caution. Low case counts have very wide 95% confidence intervals, which are the lower and upper limit within which the true rate lies 95% of the time. A narrow confidence interval leads to a more precise estimate and a wider confidence interval leads to a less precise estimate. In other words, rates calculated from very low case numbers fluctuate so much that we cannot use them to compare different areas or make predictions over time.Update Frequency: Biweekly Attributes:Ward Number – numberWard Name – textCumulative rate (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH – cumulative number of residents with confirmed COVID-19 in a Ward, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardCumulative number of cases, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward, excluding cases linked to outbreaks in LTCH and RHCumulative number of cases linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19 linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 30 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 30 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHNumber of cases in the last 30 days linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19, reported in the 30 days prior to the data pull, linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 14 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 14 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHContact: OPH Epidemiology Team

Statistics Canada and the Canadian Mortgage and Housing Corporation have developed a set of metrics on the proximity of various urban amenities to all dissemination blocks in Canada. There are 10 measures of proximity and a composite indicator that combines some of the proximity measures.The measures are based on a simple gravity model that accounts for the distance between a reference dissemination block (DB) and all the DBs in which the service is located (within a given distance) and the size of the services. The measure accounts also for the presence of services within the DB of reference.All measures, except public transit, are based on network distances between the centroids of dissemination blocks (as opposed to straight line distances). For some measures a walking network is used while for other measures a driving network is used. For public transit, the walking network distance is between the centre of a dissemination block and any public transit stop within a given range. The size of the service is captured by total employment or total revenue of the service, or more simply, the presence of points of access to the service within a given distance. The measures are released as a normalized index value, meaning that the values resulting from computations were converted to a scale from 0 to 1, where 0 indicates the lowest proximity and 1 the highest proximity in Canada. The values are normalized at the national level in order to retain as much detail as possible. This allows for more intricate analyses to be conducted on more granular geographies. Dissemination blocks with no value are those with no service within a given distance.Some measures are not available for all DBs, as the input data are not always made readily available at the desired geographic level. As the number of authoritative open data sources increases, a trend in recent years, future iterations of the proximity measures will have more comprehensive coverage spatially.More information is available from Statistics Canada. See the link provided in Supporting Documents, below.CANUE staff assigned these measures to single-link DMTI postal codes. All postal codes within a single dissemination block have the same values.

This data set contains the contact information (name, mailing address, phone number, etc.) for all publicly funded school boards and school authorities in Ontario. Data includes: * region * board number * board name * board language * board type * suite * PO box * street * city * province * postal code * phone number * fax * website Source: Board School Identification Database (BSID) / Ontario School Information System (OnSIS) as updated/maintained by boards, September 2024. Note: school board and school authority contact information are updated/maintained by boards.

Adresses Quebec is a road geobase that covers all of Quebec. It offers a complete road network including official names, addresses by slices and points, information for route management, postal codes (MOs) and a cartographic context. The user guide is available at ** next link **. For more information on the full range of products, refer to the ** directory of web services and geographic data ** of the MRNF.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The data includes information on school locations as submitted by private schools in their Notice of Intention (NOI) to operate as private school. Private schools NOI information is being collected in the Ontario School Information System (OnSIS). Data includes the following location information on private schools in Ontario. * Academic Year * School Main Location Flag * School Number * School Name * School Level Code * School Level * School Special Condition Type Code * School Special Condition Type * Latitude * Longitude * Address Type * Suite * PO Box * Street Number * Street Number Suffix * Street Name * Street Type * Rural Route Number * City * Postal Code * Province * Program Type Data excludes publicly funded schools, school authorities, publicly funded hospital and provincial schools, Education and Community Partnership Program (ECPP) facilities, summer, night, adult and continuing education schools.

This data set contains the contact information (name, mailing address, phone number, etc.) for all publicly funded school boards and school authorities in Ontario. Data includes: * region * board number * board name * board language * board type * suite * PO box * street * city * province * postal code * phone number * fax * website Source: Board School Identification Database (BSID) / Ontario School Information System (OnSIS) as updated/maintained by boards, September 2025. Note: school board and school authority contact information are updated/maintained by boards.

Facing high email bounce-backs, low CTRs, and poor response rates, while trying to target the who’s who in top companies? Our hyper-targeted and personalized CEO Email Lists but guarantee zooming ROIs, quality prospects, better brand image and long-lasting business deals. Here’s how!

We net in key CEO decision-makers undertaking major company strategic, planning, PR and execution decisions, on the strength of our segmented lists with customizable data filters like: • Job Title • Name • Email Address • Employees Size • Country • Revenue Size • Phone • Mobile and Fax • State, City, Zip Code • Website • Business Contact Number • Industry • SIC & NAICS Code • Company Name, to list a few!

This depth of data intelligence on the company’s top leadership gives Span Global Services the flexibility to undertake responsive multi-channel marketing – be it direct marketing, event marketing, email marketing, or telemarketing.

Want to bypass gatekeepers and market your products and services to the top Leadership effectively? Our targeted CEO Email Database helps you connect directly with top CEOs, with our data expertise.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

POSTAL

1,487 named individuals were selected from the Karom Group of Companies, Dialogue Canada household mail panel. This mail panel includes a cross-section of Canadians, with the exception of those living in the Yukon, Northwest Territories or Nunavut, from which a sample can be obtained to represent the Canadian population according to the most recent Statistics Canada data. The panel file was stratified by regions in Canada: city size, French Quebec and rest of Canada and ordered by postcode. The 1,487 named individuals were selected from the Dialogue Mail panel file, using a random method on the sample sorted by postcode.

Individual members of each household who were asked to complete the survey were identified by birth date and gender with this identifying information.

From the initial 1,487 mailed out, 816 questionnaires came back hence reaching a response rate of 55%.

CATI

The sample was drawn in such a way that it represented the Canadian population with the exception of the Canadians living in the Yukon, Northwest Territories or Nunavut.

The sampling model relied on the stratification of the population by ten provinces and by six community sizes. Telephone numbers were selected from the most recently published telephone directories. These numbers acted as "seeds" from which the sample was actually generated. The original "seed" telephone numbers were not used in the sample. Both unlisted numbers and numbers listed after the directory publication are included in the sample.

From within each household contacted, respondents 18 years of age and older were screened for random selection using the most recent birthday method.

From the 12,350 total calls made, 778 calls completed the interview. Among the 12,350 calls, 8,466 were ineligibles and from the latter, 5,305 calls for which the respondent was unavailable. The net response rate is therefore 24.6%.

Mode of data collection

Mail Questionnaire [mail]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.