The BAG (Basic Registrations of Addresses and Buildings) is part of the government system of basic registrations. Municipalities are source holders of the BAG. They are responsible for including the data in the BAG and for its quality. All municipalities make data about addresses and buildings available centrally via the National Facility (BAGLV). Kadaster manages the BAGLV and makes the data available to the various customers. The BAG contains information about 5 object types: buildings, residential objects, number designations, public spaces and residences. The attributes include status, surface, geometry, x-y coordinate, year of construction and purpose of use. The BAG contains this data of all buildings in the Netherlands. PLEASE NOTE: Provision costs will be charged for private parties. Check www.kadaster.nl/bag for an up-to-date delivery schedule and associated delivery costs. Linked data dataset can be accessed via https://bag.basisregistraties.overheid.nl/ REST API can be accessed via https://data.pdok.nl/bag/api/v1/

🇱🇹 리투아니아

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address registry@wa-data.se..

The National Address Register (NAR) provides users with an authoritative list of valid georeferenced civic addresses in Canada, including their corresponding mailing addresses, in a standardized address structure. NAR is made available under the Statistics Canada Open Licence agreement (https://www.statcan.gc.ca/eng/reference/licence). The GC API Store closed permanently at 12:00 EDT on September 29th, 2023. With this closure, the National Address Register (NAR) will no longer be released as an API. The NAR will continue to be released as a Pubic Use Microdata File (PUMF) via the Statistics Canada website.

Dataset contains list of address points for the whole Czech Republic in CSV format. For each address point following attributes are specified: address point code, municipality code and name, code and name of town district (for territorialy structured statutory cities only), code and name of Prague city district (for Prague only), municipality part code and name, street code and name (in case it is specified), type of building object (with description/registration house number), house number, orientation number (if it is specified), character of orientation number (if it is specified), postal code, Y and X coordinates of pointer of address point (in JTSK coordinate system) and the date of validity. Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on RÚIAN (Register of Territorial Identification, Addresses and Real Estates). Data covers the whole territory of the Czech Republic. Data is provided in a compressed form (ZIP archive). File is created during the first day of each month with data valid to the last day of previous month. More in the Act No. 111/2009 Coll., on the Basic Registers, in Decree No. 359/2011 Coll., on the Basic Register of Territorial Identification, Addresses and Real Estates.

The register of streets and addresses is kept in the IMPA (Internet Manager of Address Points) of Geo-System Sp. z o.o. and includes: locality names and location data from the state register of geographical names, the names of the streets and squares and the location of these streets and squares

identifiers of localities, streets and squares from the national register of official territorial division of the country

additional traditional names of towns, streets and squares in minority language, if circumstances occur, as referred to in Article 12 of the Act of 6 January 2005 on National and Ethnic Minorities and on Regional Language (Journal of Laws No. U. No. 17, item. 141 and No 62, item. 550 and 2009, No 31, item. 206 and No. 157, item. 1241)

address data specifying the order numbers of residential buildings and other buildings intended for permanent or temporary residence of people, including in particular buildings: office, publicly available for the purposes of culture and physical culture, of an educational nature, hospitals and medical care and intended for business, built, under construction and forecast for construction, postcodes and location of buildings in the state spatial reference system

The register of the locality of streets and addresses is kept in the IMPA (Internet Manager of Address Points) of Geo-System Sp. z o.o. and is made available using the web browsing service OGC:WMS.

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address registry.gbg@zacco.com..

🇸🇰 슬로바키아 English It contains information about the counties of the SR. The county is the highest administrative, territorial and administrative unit in Slovakia. Content of this dataset corresponds to the basic dial CL000023 — Self-governing region

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address usa.registry.center@gmail.com..

The register of streets and addresses is kept in the iMPA IT system (online Address Points Manager) of Geo-System Sp. z o.o. and includes: the names of the localities and data indicating the location of those localities from the national register of geographical names, street and square names and location data for these streets and squares identifiers of localities, streets and squares from the national register of the official territorial division of the country additional traditional names of towns, streets and squares in the minority language, if the circumstances referred to in Article 12 of the Act of 6 January 2005 on National and Ethnic Minorities and on the Regional Language (Journal of Laws No. Journal of Laws No 17, item 141 and No 62, item 550 and of 2009, No 31, item 206 and No 157, item 1241) address data identifying the serial numbers of residential buildings and other buildings intended for permanent or temporary residence of people, including in particular buildings: office, public, used for purposes of culture and physical culture, educational, hospitals and medical care and intended for economic activity, built, under construction and forecast to be built, postal codes and the location of buildings in the state spatial reference system

The register of localities of streets and addresses is kept in the ICT system IMPA (Internet Manager of Address Points) of Geo-System Sp. z o.o. and includes: city names and location data from the State Register of Geographical Names, names of streets and squares and data indicating the location of these streets and squares identifiers of localities, streets and squares from the national register of official territorial division of the country additional traditional names of localities, streets and squares in minority language, if the circumstances referred to in Article 12 of the Act of 6 January 2005 on National and Ethnic Minorities and on Regional Languages (Journal of Laws of 2005, item 12) apply. U. No. 17, item. 141 and 62, item 550 and 2009 No 31, item 206 and No 157, item 1241) address details indicating the serial numbers of residential buildings and other buildings intended for the permanent or temporary presence of persons, including in particular buildings: office, publicly accessible for the purposes of physical culture and culture, of educational nature, hospitals and medical care and intended for economic activity, built, under construction and projected for construction, postcodes and the location of buildings in the state system of spatial references

The register of the locality of streets and addresses is kept in the IMPA (Internet Manager of Address Points) of Geo-System Sp. z o.o. and is made available using the web browsing service OGC:WMS.

The Rental Registry is a Periodic Inspection Program of the Division of Code Enforcement (DOCE). Owners of any one- or two-family non-owner occupied dwellings rented or leased within the City of Syracuse are required to apply for and obtain a Rental Registry Certificate every 3 years. Please note that the data displayed here are limited and may not fully represent all Rental Registry status details. Please refer to our FAQ page for additional help, and contact the DOCE Periodic Inspection Coordinator at mhigh@syrgov.net or (315) 448-4700 to: Request an eligible property be added to the programRequest application, status, or inspection detailsIf you cannot find the information you’re looking for here Data Dictionary:Label: PropertyAddressDefinition: Parcel street number and street address.Label: parcel_idDefinition: Unique parcel identifier.Label: ZIPDefinition: Zip code of parcels street addressLabel: SBLDefinition: Stands for Section, Block, Lot. This is a property tax number unique to this property.Label: VacantDefinition: Y = Determined vacant by the Division of Code EnforcementLabel: NeedsRRDefinition: Whether a parcel is assigned the user defined "Rental Registry Required," this is added to properties that are 1-2 family non-owner occupiedLabel: transaction_to_property_idDefinition: Unique number that joins this property to its owner information.Label: completion_dateDefinition: The date that the completion type was entered on a Rental Registry caseLabel: completion_type_nameDefinition: The two completion types associated with the Rental Registry are "Rental Registry Card Issued" and "Family Based Exemption Granted"Label: inpsection_periodDefinition: The date associated with the Rental Registry case (this is updated manually at different periods for different reasons -- it could be the date a new case/inspection period was created, the date of an upcoming scheduled inspection, or the date that the property is coming due to reapply -- may not be hugely useful for the public without access to all of the case details)Label: insp_comp_is_activeDefinition: Whether or not the Rental Registry of any given property is currently valid.Label: valid_untilDefinition: The date that the current Rental Registry certificate will expire - this is 3 years from the date that the last completion (RR card or family based exemption) was issued.Label: rr_is_validDefinition: Whether or not the Rental Registry of any given property is currently valid.Label: LatitudeDefinition: Latitude of this parcel.Label: LongitudeDefinition: Longitude of this parcel.Label: ObjectIDDefinition: Unique identifier, assigned by ArcGIS to the dataset.Dataset Contact Information:Organization: City of Syracuse - Office of Accountability, Performance, and Innovation (API)Position: Data Program ManagerCity: Syracuse, NYE-Mail Address: opendata@syrgov.net

Comprehensive dataset of 1 Registry offices in Mie, Japan as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Abstract

The household registration system known as ho khau has been a part of the fabric of life in Vietnam for over 50 years. The system was used as an instrument of public security, economic planning, and control of migration, at a time when the state played a stronger role in direct management of the economy and the life of its citizens. Although the system has become less rigid over time, concerns persist that ho khau limits the rights and access to public services of those who lack permanent registration in their place of residence. Due largely to data constraints, however, previous discussions about the system have relied largely on anecdotal or partial information.

Drawing from historical roots as well as the similar model of China’s hukou, the ho khau system was established in Vietnam in 1964. The 1964 law established the basic parameters of the system: every citizen was to be registered as a resident in one and only household at the place of permanent residence, and movements could take place only with the permission of authorities. Controlling migration to cities was part of the system’s early motivation, and the system’s ties to rationing, public services, and employment made it an effective check on unsanctioned migration. Transfer of one’s ho khau from one place to another was possible in principle but challenging in practice.

The force of the system has diminished since the launch of Doi Moi as well as a series of reforms starting in 2006. Most critically, it is no longer necessary to obtain permission from the local authorities in the place of departure to register in a new location. Additionally, obtaining temporary registration status in a new location is no longer difficult. However, in recent years the direction of policy changes regarding ho khau has been varied. A 2013 law explicitly recognized the authority of local authorities to set their own policies regarding registration, and some cities have tightened the requirements for obtaining permanent status.

Understanding of the system has been hampered by the fact that those without permanent registration have not appeared in most conventional sources of socioeconomic data. To gather data for this project, a survey of 5000 respondents in five provinces was done in June-July 2015. The samples are representative of the population in 5 provinces – Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong and Dak Nong. Those five provinces/cities are among the provinces with the highest rate of migration as estimated using data from Population Census 2009.

Geographic coverage

5 provinces – Ho Chi Minh City, Ha Noi, Da Nang, Binh Duong and Dak Nong.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling for the Household Registration Survey was conducted in two stages. The two stages were selection of 250 enumeration areas (50 EAs in each of 5 provinces) and then selection of 20 households in each selected EA, resulting in a total sample size of 5000 households. The EAs were selected using Probability Proportional to Size (PPS) method based on the square number of migrants in each EA, with the aim to increase the probability of being selected for EAs with higher number of migrants. “Migrants” were defined using the census data as those who lived in a different province five years previous to the census. The 2009 Population Census data was used as the sample frame for the selection of EAs. To make sure the sampling frame was accurate and up to date, EA leaders of the sampled EAs were asked to collection information of all households regardless of registration status at their ward a month before the actual fieldwork. Information collected include name of head of household, address, gender, age of household’s head, household phone number, residence registration status of household, and place of their registration 5 years ago. All households on the resulting lists were found to have either temporary or permanent registration in their current place of residence.

Using these lists, selection of survey households was stratified at the EA level to ensure a substantial surveyed population of households without permanent registration. In each EA random selection was conducted of 12 households with temporary registration status and 8 households with permanent registration status. For EAs where the number of temporary registration households was less than 12, all of the temporary registration households were selected and additional permanent registration households were selected to ensure that each EA had 20 survey households. Sampling weights were calculated taking into the account the selection rules for the first and second stages of the survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was mostly adapted from the Vietnam Household Living Standard Survey (VHLSS), and the Urban Poverty Survey (UPS) with appropriate adjustment and supplement of a number of questions to follow closely the objectives of this survey. The household questionnaire consists of a set of questions on the following contents:

• Demographic characteristics of household members with emphasis on their residence status in terms of both administrative management (permanent/temporary residence book) and real residential situation. • Education of household members. Beside information on education level, the respondents are asked whether a household member attend school as “trai-tuyen” , how much “trai-tuyen” fee/enrolment fee, and difficulty in attending schools without permanent residence status. • Health and health care, collecting information on medical status and health insurance card of household members. • Labour and employment, asking household member’s employment status in the last 30 days; their most and second-most time-consuming employment during the last 30 days; and whether they had been asked about residence status when looking for job. • Assets and housing conditions. This section collects information on household’s living conditions such as assets, housing types and areas, electricity, water and energy. • Income and expenditure of households. • Social inclusion and protection. The respondents are asked whether their household members participate in social organizations, activities, services, contribution; whether they benefit from any social project/policy; do they have any loans within the last 12 months; and to provide information about five of their friends at their residential area. • Knowledge on the Law of Residence, current regulations on conditions for obtaining permanent residence, experience dealing with residence issues, and opinion on current household registration system of the respondents.

Cleaning operations

Managing and Cleaning the Data

Data were managed and cleaned each day immediately upon being received, which occurred at the same time as the fieldwork surveys. At the end of each workday, the survey teams were required to review all of the interviews conducted and transfer collected data to the server. The data received by the main server were downloaded and monitored by MDRI staff.

At this stage, MDRI assigned a technical team to work on the data. First, the team listened to interview records and used an application to detect enumerators’ errors. In this way, MDRI quickly identified and corrected the mistakes of the interviewers. Then the technical team proceeded with data cleaning by questionnaire, based on the following quantity and quality checking criteria.

• Quantity checking criteria: The number of questionnaires must be matched with the completed interviews and the questionnaires assigned to each individual in the field. According to the plan, each survey team conducted 20 household questionnaires in each village. All questionnaires were checked to ensure that they contained all essential information, and duplicated entries were eliminated. • Quality checking criteria: Our staff performed a thorough examination of the practicality and logic of the data. If there was any suspicious or inconsistent information, the data management team re – listened to the records or contacted the respondents and survey teams for clarification via phone call. Necessary revisions would then be made.

Data cleaning was implemented by the following stages: 1. Identification of illogical values; 2. Software – based detection of errors for clarification and revision; 3. Information re-checking with respondents and/or enumerators via phone or through looking at the records; 4. Development and implementation of errors correction algorithms; The list of detected and adjusted errors is attached in Annex 6.

Outlier detection methods The data team applied a popular non - parametric method for outlier detection, which can be done with the following procedure: 1. Identify the first quartile Q1 (the 25th percentile data point) 2. Identify the third quartile Q3 (the 75th percentile data point) 3. Identify the inter-quartile range(IQR): IQR=Q3-Q1 4. Calculate lower limits (L) and upper limits (U) by the following formulas: o L=Q1-1.5*IQR o U=Q3+1.5*IQR 5. Detect outliers by the rule: An observation is an outlier if it lies below the lower bound or beyond the upper bound (i.e. less than L or greater than U)

Data Structure The completed dataset for the “Household registration survey 2015” includes 9 files in STATA format (.dta): • hrs_maindata: Information on the households, including: assets, housing, income, expenditures, social inclusion and social protection issues, household registration procedures • hrs_muc1: Basic information on the

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address registry-operations@snapnames.com..

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address registry@sverige.net..

Region, number of village, current address, household registration in the village, current address, household registration in the neighborhood, household number, gender, population, total number of immigrants, immigrants from foreign countries, immigrants from other cities and provinces in New Taipei City, immigrants from other cities and provinces in Taipei City, immigrants from other cities and provinces in Taoyuan City, immigrants from other cities and provinces in Taichung City, immigrants from other cities and provinces in Tainan City, immigrants from other cities and provinces in Kaohsiung City, immigrants from other cities and provinces in Taiwan Province, immigrants from other cities and provinces in Fujian Province, immigrants from other cities and provinces in other provinces and cities, immigrants from other counties and cities in the province, immigrants from other townships and cities in the county, initial household registration, other, total number of emigrants, emigrants abroad, emigrants to other cities and provinces in New Taipei City, emigrants to other cities and provinces in Taipei City, emigrants to other cities and provinces in Taoyuan City, emigrants to other cities and provinces in Taichung City, emigrants to other cities and provinces in Tainan City, emigrants to other cities and provinces in Kaohsiung City, emigrants to other cities and provinces in Taiwan Province, emigrants to other cities and provinces in Fujian Province, emigrants to other cities and provinces in other provinces and cities, emigrants to other counties and cities in the province, emigrants to other townships and cities in the county, cancellation of household registration, other emigrants, number of address changes, number of addresses changes, increase in the number of administrative area adjustments, decrease in the number of administrative area adjustments, total number of infant births, total number of infant births from marriage, total number of infant births from recognized non-marital relationships, total number of infant births from unrecognized non-marital relationships, total number of unaccompanied children, birth mother's original nationality - mainland China, Hong Kong, Macao, birth mother's original nationality outside mainland China, birth father's original nationality - mainland China, Hong Kong, Macao, birth father's original nationality outside mainland China, posthumous child, twins, triplets or more, number of deaths, number of adoptions, number of adoptions terminated, number of guardians, number of assistants, number of minors burdened with rights and obligations, total number of marriages, total number of marriages between different genders, total number of marriages between the same gender, total number of divorces and terminations of marriages, total number of divorces and terminations of marriages between different genders, total number of divorces and terminations of marriages between the same gender

Patient Registry Software Market Outlook

According to our latest research, the global patient registry software market size reached USD 1.92 billion in 2024, reflecting robust expansion amid digital transformation in healthcare. The market is projected to grow at a CAGR of 13.1% from 2025 to 2033, reaching a forecasted value of USD 5.41 billion by 2033. Growth is primarily fueled by the rising need for real-time patient data management, regulatory mandates for disease registries, and the integration of advanced analytics and artificial intelligence within healthcare settings.

A key growth driver for the patient registry software market is the escalating demand for comprehensive data collection and analysis to support evidence-based clinical decision-making. Healthcare providers are increasingly leveraging patient registry software to improve patient outcomes, streamline workflows, and facilitate population health management. The growing prevalence of chronic diseases such as diabetes, cardiovascular disorders, and cancer underscores the need for robust patient registries that can track patient journeys, monitor treatment efficacy, and enable proactive interventions. Additionally, the shift towards value-based care models and the need to demonstrate outcomes for reimbursement further amplify the adoption of these platforms.

Another significant factor propelling market growth is the evolving regulatory landscape. Governments and healthcare authorities across the globe are mandating the collection and reporting of patient data for disease surveillance, public health management, and clinical research. Initiatives such as the U.S. 21st Century Cures Act and the European General Data Protection Regulation (GDPR) have heightened the focus on data interoperability, security, and patient privacy, prompting healthcare organizations to invest in sophisticated registry solutions. These regulatory requirements not only ensure compliance but also foster greater transparency and accountability in patient care, driving the adoption of patient registry software across various healthcare settings.

Technological advancements are also shaping the trajectory of the patient registry software market. The integration of artificial intelligence, machine learning, and cloud computing has revolutionized the capabilities of registry platforms, enabling real-time analytics, predictive modeling, and seamless data exchange across disparate systems. The proliferation of cloud-based and web-based deployment models offers scalability, cost-efficiency, and remote accessibility, making advanced registry solutions more accessible to small and medium healthcare organizations. Furthermore, the rising trend of patient-centric care and personalized medicine relies heavily on robust data aggregation and analytics, further cementing the role of patient registry software as a critical enabler of next-generation healthcare delivery.

Rare Disease Registries play a crucial role in the patient registry software market, particularly as healthcare systems strive to address the unique challenges posed by rare diseases. These registries are designed to collect and manage comprehensive data on patients with rare conditions, facilitating research, diagnosis, and treatment development. By aggregating data from diverse sources, rare disease registries enable healthcare providers to identify patterns, track disease progression, and evaluate treatment outcomes. This is especially important given the limited patient populations and the need for specialized care pathways. The integration of rare disease registries with advanced analytics and artificial intelligence further enhances their utility, providing insights that can drive personalized medicine and improve patient outcomes. As awareness and advocacy for rare diseases continue to grow, the demand for robust registry solutions in this area is expected to increase, offering significant opportunities for innovation and market expansion.

From a regional perspective, North America dominates the global patient registry software market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to its advanced healthcare infrastructure, high adoption of electronic health records (EHRs), and extensive regulatory support for health IT initiatives. However, Asia Pacific