The Database Platform as a Service (DBPaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for data analytics. The market's expansion is fueled by businesses migrating legacy on-premise databases to cloud-based alternatives, seeking enhanced agility, and leveraging the advantages of pay-as-you-go models. Major players like Amazon Web Services, Microsoft Azure, and Google Cloud Platform dominate the market, offering a wide range of DBPaaS options catering to diverse needs, from relational databases to NoSQL solutions. The market is segmented by deployment model (public cloud, private cloud, hybrid cloud), database type (SQL, NoSQL, NewSQL), and industry vertical (BFSI, healthcare, retail, etc.). Competition is fierce, with established players constantly innovating and new entrants emerging to challenge the status quo. Factors like data security concerns and integration complexities pose some challenges to market growth. However, advancements in serverless computing and the increasing adoption of artificial intelligence (AI) and machine learning (ML) are expected to drive further expansion. The forecast period (2025-2033) is projected to witness substantial growth, driven by ongoing digital transformation initiatives across various industries. The increasing adoption of cloud-native applications and microservices architectures further necessitates robust and scalable DBPaaS solutions. While the initial investment in migrating to the cloud can be significant, the long-term cost savings and improved efficiency make DBPaaS an attractive option. The market's growth is expected to be particularly strong in regions with high cloud adoption rates and robust digital infrastructure. The competitive landscape will likely remain dynamic, with mergers and acquisitions, strategic partnerships, and continuous product innovation shaping the market's trajectory. Overall, the DBPaaS market is poised for substantial growth, driven by a confluence of technological advancements and evolving business needs. Assuming a conservative CAGR of 20% (a reasonable estimate considering the high growth sectors involved), and a 2025 market size of $50 Billion, we can project substantial future growth.

Database Management System Market Outlook

According to our latest research, the global Database Management System (DBMS) market size reached USD 85.5 billion in 2024, reflecting the sector’s robust expansion across various industries. The market is expected to grow at a CAGR of 11.8% from 2025 to 2033, culminating in a forecasted market size of USD 231.7 billion by 2033. This impressive growth is primarily driven by the escalating volume of data generated by digital transformation initiatives, rising adoption of cloud-based solutions, and the increasing complexity of enterprise data ecosystems.

One of the key growth factors for the Database Management System market is the proliferation of big data analytics and the need for real-time data processing. Organizations across sectors such as BFSI, healthcare, retail, and manufacturing are leveraging advanced DBMS solutions to derive actionable insights from massive datasets. The integration of artificial intelligence and machine learning into database management systems is further enhancing their analytical capabilities, enabling predictive analytics, automated data governance, and anomaly detection. As businesses continue to digitize their operations, the demand for scalable, secure, and high-performance DBMS platforms is expected to surge, fueling market expansion.

Another significant driver is the widespread migration to cloud-based database architectures. Enterprises are increasingly opting for cloud deployment due to its flexibility, cost-effectiveness, and ease of scalability. Cloud-based DBMS solutions allow organizations to manage data across multiple geographies with minimal infrastructure investment, supporting global expansion and remote work trends. The growth of hybrid and multi-cloud environments is also propelling the need for database management systems that can seamlessly integrate and synchronize data across diverse platforms. This shift is compelling vendors to innovate and offer more robust, cloud-native DBMS offerings.

The evolution of database types, particularly the rise of NoSQL and in-memory databases, is transforming the DBMS market landscape. Traditional relational databases are now complemented by NoSQL databases that cater to unstructured and semi-structured data, supporting use cases in IoT, social media, and real-time analytics. In-memory databases, known for their low latency and high throughput, are gaining traction in applications requiring instantaneous data access. This diversification of database technologies is enabling organizations to choose best-fit solutions for their specific needs, contributing to the overall growth and dynamism of the market.

From a regional perspective, North America dominates the Database Management System market due to its advanced IT infrastructure, high cloud adoption rates, and strong presence of major technology providers. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization in emerging economies, increasing investments in IT modernization, and the expansion of e-commerce and fintech sectors. Europe, Latin America, and the Middle East & Africa are also experiencing steady growth, supported by regulatory compliance initiatives and the modernization of legacy systems. The global nature of data-driven business models ensures that demand for sophisticated DBMS solutions remains strong across all regions.

Component Analysis

The Database Management System market by component is segmented into software and services, each playing a pivotal role in the overall ecosystem. The software segment encompasses various types of DBMS platforms, including relational, NoSQL, and in-memory databases, which form the backbone of enterprise data management strategies. This segment holds the largest market share, driven by continuous innovations in database architectures, enhanced security features, and integration capabilities with emerging technologies such as AI and IoT. Organizations are increasingly investing in advanced DBMS software to manage the growing complexity and volume of data, ensure data integrity, and support mission-critical applications.

On the other hand, the services segment, which includes consulting, implementation, support, and maintenance, is experiencing rapid growth as enterprises seek to optimize their database environments. The complexity of modern database systems necessitates expert

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

Reproducibility data for the AntiBody Sequence Database (ABSD) article. This dataset contains the raw data (antibody sequences) extracted on June 20, 2024, from various databases, as well as the several scripts, to ensure the reproducibility of our results. External databases used: ABDB, AbPDB, CoV-AbDab, Genbank, IMGT, PDB, SACS, SAbDab, TheraSAbDab, UniProt, KABAT Scripts usage: each external database has a corresponding script to format all antibody sequences extracted from it. A last script enable merging all extracted antibody sequences while removing redundancy, standardizing and cleaning data.

Embedded Database Management Systems Market size was valued at USD 10.8 Billion in 2024 and is projected to reach USD 18.70 Billion by 2031, growing at a CAGR of 7.1% during the forecasted period 2024 to 2031.

The Embedded Database Management Systems (DBMS) market is driven by the increasing demand for real-time data processing and management across various embedded systems, such as IoT devices, smartphones, automotive systems, and industrial equipment. The rise of connected devices and edge computing has amplified the need for lightweight, efficient, and scalable embedded databases that can operate within resource-constrained environments. Growing adoption of embedded systems in industries like healthcare, automotive, telecommunications, and consumer electronics is also boosting the demand for robust DBMS solutions. Additionally, advancements in AI, machine learning, and data analytics are driving the integration of more sophisticated embedded databases to enable real-time decision-making and enhance device performance.

According to Cognitive Market Research, the global In-Memory Database market size was USD 7.8 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 19.1% from 2024 to 2031. Market Dynamics of In-Memory Database Market

Key Drivers for In-Memory Database Market

Increasing Volume of Data - The exponential growth of data generated by various sources, including social media, IoT devices, and enterprise applications, is another key driver for the IMDB market. Organizations are increasingly seeking efficient ways to manage and analyze this vast amount of data to gain actionable insights and maintain a competitive edge. In-memory databases are well-suited to handle large volumes of data with high throughput, providing the scalability needed to accommodate the growing data influx. The ability to scale horizontally by adding more nodes to the database cluster ensures that IMDBs can meet the demands of data-intensive applications.
The increasing dependence on real-time analytics and decision-making is anticipated to drive the In-Memory Database market's expansion in the years ahead.

Key Restraints for In-Memory Database Market

The amount of available RAM, which can restrict their scalability for very large datasets, limits the In-Memory Database industry growth.
The market also faces significant difficulties related to the high cost of implementation.

Introduction of the In-Memory Database Market

The In-Memory Database market is experiencing robust growth, driven by the need for high-speed data processing and real-time analytics across various industries. In-memory databases store data directly in the main memory (RAM) rather than on traditional disk storage, allowing for significantly faster data retrieval and manipulation. This technology is particularly advantageous for applications requiring rapid transaction processing and real-time data insights, such as financial services, telecommunications, and e-commerce. Despite its benefits, the market faces challenges, including high implementation costs and limitations on data storage capacity due to RAM constraints. Additionally, concerns about data volatility and the need for continuous power supply further complicate adoption. However, advancements in memory technology, declining costs of RAM, and the increasing demand for real-time analytics are driving market growth. As businesses seek to enhance performance and decision-making capabilities, the In-Memory Database market is poised for continued expansion, providing critical solutions for high-performance data management.

It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.

I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.

The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.

Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country

Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries

Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.

Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC

Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.

Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

The Swedish Contextual Database provides a large number of longitudinal and regional macro-level indicators primarily assembled to facilitate research on the effects of contextual factors on family and fertility behavior. It can be linked to the individual-level data of the Swedish GGS as well as to data of other surveys. It can also be used for other types of research and for teaching. The comparative data will also be integrated into the international Contextual Database of the GGP. The contextual data are available open-access through the GGP webpage: www.ggp-i.org and through the webpage of Stockholm University Demography Unit www.suda.su.se

Purpose:

The Swedish contextual database (CDB) was established to accompany the Swedish Generations and Gender Survey (GGS) and to complement the contextual database of the international Generations and Gender Programme (GGP).

The Swedish Contextual Data Collection is available in xls format. In addition to that, the internationally comparative data will be integrated into the Contextual Database (CDB) of the GGP in 2018. These data can be exported in other formats, as well (e.g. CSV, XML). The indicators can also be accessed in a single file in STATA or SPSS format. The data can be matched with the Swedish GGS. International regional coding schemes are also supported, such as NUTS, OECD.

The National Bioscience Database Center (NBDC) intends to integrate all databases for life sciences in Japan, by linking each database with expediency to maximize convenience and make the entire system more user-friendly. We aim to focus our attention on the needs of the users of these databases who have all too often been neglected in the past, rather than the needs of the people tasked with the creation of databases. It is important to note that we will continue to honor the independent integrity of each database that will contribute to our endeavor, as we are fully aware that each database was originally crafted for specific purposes and divergent goals. Services: * Database Catalog - A catalog of life science related databases constructed in Japan that are also available in English. Information such as URL, status of the database site (active vs. inactive), database provider, type of data and subjects of the study are contained for each database record. * Life Science Database Cross Search - A service for simultaneous searching across scattered life-science databases, ranging from molecular data to patents and literature. * Life Science Database Archive - maintains and stores the datasets generated by life scientists in Japan in a long-term and stable state as national public goods. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear terms of use. * Taxonomy Icon - A collection of icons (illustrations) of biological species that is free to use and distribute. There are more than 200 icons of various species including Bacteria, Fungi, Protista, Plantae and Animalia. * GenLibi (Gene Linker to bibliography) - an integrated database of human, mouse and rat genes that includes automatically integrated gene, protein, polymorphism, pathway, phenotype, ortholog/protein sequence information, and manually curated gene function and gene-related or co-occurred Disease/Phenotype and bibliography information. * Allie - A search service for abbreviations and long forms utilized in life sciences. It provides a solution to the issue that many abbreviations are used in the literature, and polysemous or synonymous abbreviations appear frequently, making it difficult to read and understand scientific papers that are not relevant to the reader's expertise. * inMeXes - A search service for English expressions (multiple words) that appear no less than 10 times in PubMed/MEDLINE titles or abstracts. In addition, you can easily access the sentences where the expression was used or other related information by clicking one of the search results. * HOWDY - (Human Organized Whole genome Database) is a database system for retrieving human genome information from 14 public databases by using official symbols and aliases. The information is daily updated by extracting data automatically from the genetic databases and shown with all data having the identifiers in common and linking to one another. * MDeR (the MetaData Element Repository in life sciences) - a web-based tool designed to let you search, compare and view Data Elements. MDeR is based on the ISO/IEC 11179 Part3 (Registry metamodel and basic attributes). * Human Genome Variation Database - A database for accumulating all kinds of human genome variations detected by various experimental techniques. * MEDALS - A portal site that provides information about databases, analysis tools, and the relevant projects, that were conducted with the financial support from the Ministry of Economy, Trade and Industry of Japan.

IMOPE is the reference database for buildings at national level. To date and on a daily basis, it supports nearly 20,000 public and private actors and more than 800 territories (in operational context: fight against unworthy housing, fight against vacancy, energy renovation, OPAH-RU, PIG, VOC,...) wanting to know and transform the French building sector.

Resulting from public research conducted at Mines Saint-Etienne (Institut Mines Télécom), this breakthrough innovation, the methods of which have been patented by the Ministry of the Economy, Industrial and Digital Sovereignty, brings together all the data of interest (+ 250 items of information) on each of the 20 million existing buildings.

⁇ Consult the news of the ONB and the national IMOPE database ⁇ ACTU ONB/IMOPE

IMOPE has been co-built, since its creation in 2016, with and for the actors of the territories (ALEC, operators ANAH, ADIL, DDT, ADEME, EPCI, urban planning agencies ...) in order to meet the multiple challenges of the building sector. Issues on which we can cite:energy renovation, combating vacancy, precariousness and unsanitary conditions, attrition of housing, home support, adaptation to climate change, etc.

The sourcing of merged and reprocessed data: A single and multiple sourcing to increase knowledge and merging in particular: - Open Data: BAN, BDTOPO, DVF, DPE (ADEME), consumption data (ENEDIS, GRDF), RPLS, QPV, Georisks, permanent equipment base, SITADEL, socio-economic data (RP, FiLoSoFi, INSEE), OPAH, ... - "Conventional" data: Land files enriched by Cerema (source DGFiP DGALN), LOVAC, non-anonymised data of owners, RNIC (ANAH) - Local or business data: devices, FSL, LHI, orders, procedures, reporting, planning permission, rental permit, ANAH aid, ... - "Enriched" data: Machine Learning and Deep Learning (DVF, DPE, power source and heating type predictions)

A strong commitment to the commons: U.R.B.S, spin-off of Mines Saint-Etienne, maintains, develops and improves on a clean background and since 2019 the IMOPE database. With a view to mutualisation and openness, U.R.B.S. invites the entire building community (architects, public decision-makers, insurers, artisans, diagnosticians, researchers, citizens, design offices, etc.) to disseminate and reuse widely internally as well as externally, natively or with post-processing, the data contained in the IMOPE database.

It is driven by this philosophy of sharing that we have deployed the**National Building Observatory** (ONB). The**ONB** is a citizen geo-common. As a decision-making tool providing knowledge of the building stock, it makes it easier for everyone to access the information contained in the national IMOPE database.

Convinced that together we will go further, the ONB and IMOPE are initiatives led by civil society. Civil society of which we are part and which, we are convinced, is the keystone for achieving the energy, climate and social objectives of the building sector.

⁇ For more information: https://www.urbs.fr ⁇ To contact us: contact@urbs.fr ⁇ To access the ONB: https://app.urbs.fr/onb/connection

⁇ To access the data catalogue, click here

Georeferenced vector database, containing the areas bearing reports of travertine deposits, mainly associated with springs or "Limestone Precipitating Springs", detected at a scale of 1:10,000 in the Emilia-Romagna Apennines and approximated to polygons. In the tabular content, unpublished data (taken from the Author's personal knowledge) are differentiated from those taken from existing databases, such as for example the regional databases of the Geological Map of the Emilia-Romagna Apennines at 1:10,000 scale or of the Habitat map of Emilia-Romagna. The stratigraphic-structural domains to which the geological units within which the reports are found belong are also indicated.

Database of Genotype and Phenotype (dbGaP) was developed to archive and distribute the data and results from studies that have investigated the interaction of genotype and phenotype in Humans.

Download .zipMaps and data associated with oil-and-gas wells represent one of the largest datasets at the Ohio Department of Natural Resources. This GIS data layer contains all the locatable oil-and-gas wells in Ohio. The feature is derived from coordinates obtained from the Division of Oil and Gas Resources Management (DOGRM) oil and gas well database – Risk Based Data Management System (RBDMS). The RBDMS database has a long history and is a comprehensive collection of well data from historic pre-1980 paper well records (digitized by the Division of Geological Survey (DGS)) to post-1980 DOGRM database solutions.Since 1860, it is estimated that more than 267,000 oil-and-gas wells have been drilled in Ohio. The compressed file also includes a feature used to connect the surface location to the bottom location of a well that has been drilled directionally or horizontally. This feature is NOT the actual wellbore path, it is simply a graphical representation indicating the relationship between the two well points.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesDivision of Oil & Gas ResourcesOil and Gas Resources Management2045 Morse Road Bldg F-2Columbus, OH, 43229-6693Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov Data Update Frequency: Every Saturday

Database survey

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Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

New version 2.0.0 with majors change

For free and complete informations concerning CASSMIR datasets, please visit our website (in French).

The CASSMIR database (Contribution to the Spatial and Sociological Analysis of Residential Real Estate Markets) is a spatial and population datasets on housing property market of the Parisian metropolitan area, from 1996 to 2018. The indicators in the CASSMIR database cover four "thematic areas of investigation" : prices, socio-demographic profile of buyers and sellers, purchasing regimes and types of property transfers and types of real estate. These indicators characterize spatial units at three scales (communal level, 1km grid and 200m grid) and population groups of buyers and sellers declined according to social, generational and gender criteria. The delivery of the database follows a series of matching and aggregation of individual data from two original databases : a database on real estate transactions (BIEN database) and a database on first-time buyer investments (PTZ database). CASSMIR delivers aggregated data (with nearly 350 variables) in open access for non-commercial use.

This repository consists of sevenfiles.

"CASSMIR_SpatialDataBase" is a Geopackage file, it lists all the data aggregated to spatial units of reference. It is composed of three layers that correspond to the geographical scale of aggregation: at a communal level, a grid of one kilometer on each side and a grid of two hundred meters on each side.

"CASSMIR_GroupesPopDataBase" is a .csv file, it lists all the data aggregated to population groups of reference. There are three types of population groups : groups referenced by the social position of the buyers/sellers (social group), groups referenced by the age group to which the buyers/sellers belong (generational group), groups referenced by the sex of the buyers/sellers (gender group).

Two metadata files (.csv) lists the metadata of the indicators made available. They are systematically structured as follows :

• Id_var: the identifier of the variable contained in "CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase"
• Unite d'observation des variables descriptives : descriptive units of observation (Prices, buyers, sellers...)
• Type d'information : precision on the type of information
• Label : Description of the contents of the variable
• Indicator_Group: The group of indicators to which the variable relates (prices, socio-demographics indicators of buyers and sellers...)
• Unit : The unit of measurement of the variable
• Spatial_Availability : A precision on the availability of the variable in the spatial database (communes, 1 km grid and 200m grid)
• GroupesPop_Availability : A precision on the availability of the variable in the population groupes database (Social, generational , gender)
• Data_Source: The main origin of the data (INSEE, BIEN and/or PTZ)
• Remarques : possible remarks on the construction of the variable

"BIENSampleForTest" and "PTZSampleForTest" are two .txt files which restore a sample of individual data from each of the original databases. All data were anonymized and the values randomized. These two files are specifically dedicated to reproducing the different stages of processing that lead to the production of the CASSMIR files ("CASSMIR_SpatialDataBase" or "CASSMIR_GroupesPopDataBase") and cannot be used in any other way.

"LEXIQUE" is a glossary of terms used to name the variables (.csv).

The creation of the database was funded by the National Reseach Agency (ANR WIsDHoM https://anr.fr/Projet-ANR-18-CE41-0004).

All CASSMIR documentation (in French) and R codes are accessible via the Gitlab repository at the following address : https://gitlab.huma-num.fr/tlecorre/cassmir.git

METADATA :

• Licence

This dataset is registered under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to copy, distribute, transmit, and adapt the data, provided that you give credit to the CASSMIR data base and specify the original source of the data. If you modify or use the data in other derivative works, you may distribute them only under the same license. You may not make commercial use of this database, nor may you use it for any purpose other than scientific research.

• Citation standard

- Figures: (CC - CASSMIR database, indicator(s) constructed from XXX data)

- Bibliography : Productions that use the CASSMIR database must reference the dataset and the data paper.

Dataset: Le Corre T., 2020, CASSMIR (Version 2.0.0) [Data set], Zenodo. http://doi.org/10.5281/zenodo.4497219

Data paper: Thibault Le Corre, « Une base de données pour étudier vingt années de dynamiques du marché immobilier résidentiel en Île-de-France », Cybergeo: European Journal of Geography [En ligne], Data papers, article No.992, mis en ligne le 09 août 2021. URL : http://journals.openedition.org/cybergeo/37430 ; DOI : https://doi.org/10.4000/cybergeo.37430

• Data paper title

"Une base de données pour étudier vingt années de dynamiques du marché immobilier en Île-de-France"

• Author

Thibault Le Corre

• Keywords

Housing market, data base, Île-de-France, spatio-temporal dynamics

• Related Publication

DOI : https://doi.org/10.4000/cybergeo.37430

• Language

French

• Time Period Covered

The time period covered by the indicators in the database depends on the data sources used, respectively:
For data from BIEN: 1996, 1999, 2003-2012, 2015, 2018
For data from PTZ: 1996-2016

• Kind of data

Nature of data submitted

• vector: Vector data

• grid: Data mesh

• code: programming code (see the website or GitLab of the project)

• Data Sources

Base BIEN

Base PTZ

• Geographical Coverage

Île-de-France region

• Geographical Unit

Municipalities and grid mesh elements (1km side grid and 200 side grid) concerned by real estate transactions

• Geographic Bounding Box

Reference Coordinate System (RCS): EPSG 2154 RGF93/Lambert 93.

- Xmin : 586421.7
- Xmax : 741205.6
- Ymin : 6780020
- Ymax : 6905324

• Type of article

Data Paper

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the _location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. This update features the most recent geographic boundaries (2019 Census Block Groups) and new and expanded sources of data used to calculate variables. Entirely new variables have been added and the methods used to calculate some of the SLD variables have changed. More information on the National Walkability index: https://www.epa.gov/smartgrowth/smart-location-mapping More information on the Smart Location Calculator: https://www.slc.gsa.gov/slc/