Chile INE Projection: Population: Araucania: Tolten data was reported at 9.643 Person th in 2035. This records a decrease from the previous number of 9.671 Person th for 2034. Chile INE Projection: Population: Araucania: Tolten data is updated yearly, averaging 10.087 Person th from Jun 2002 (Median) to 2035, with 34 observations. The data reached an all-time high of 11.606 Person th in 2002 and a record low of 9.643 Person th in 2035. Chile INE Projection: Population: Araucania: Tolten data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G002: Population: Projection.

Chile INE Projection: Population: Araucania: Pitrufquen data was reported at 27.592 Person th in 2035. This records an increase from the previous number of 27.528 Person th for 2034. Chile INE Projection: Population: Araucania: Pitrufquen data is updated yearly, averaging 25.825 Person th from Jun 2002 (Median) to 2035, with 34 observations. The data reached an all-time high of 27.592 Person th in 2035 and a record low of 22.792 Person th in 2002. Chile INE Projection: Population: Araucania: Pitrufquen data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G002: Population: Projection.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 NOAA Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. The SFWMD manages the water resources of various interconnected areas in south Florida, which are defined in the SFWMD ArcHydro Enhanced Database (AHED) as “AHED Rain Areas”. The SFWMD is interested in summarizing change factors for each individual AHED Rain Area to use in future planning efforts. Geospatial data provided in an ArcGIS shapefile named “AHED_basins.shp” are described herein. The shapefile contains polygons for the AHED Rain Areas defined in the South Florida Water Management District (SFWMD)'s ArcHydro Enhanced Database (AHED) including their acreages.

This data release is the update of the U.S. Geological Survey - ScienceBase data release by Bera (2020), with the processed data through September 30, 2020. The primary data for water year 2020 (a water year is the 12-month period, October 1 through September 30, in which it ends) is downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2020) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) in thousandths of an inch is computed from average daily air temperature in degrees Fahrenheit (°F), average daily dewpoint temperature in degrees Fahrenheit (°F), daily total wind movement in miles (mi), and daily total solar radiation in Langleys per day (Lg/d) and disaggregated to hourly PET in thousandths of an inch using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as "backup". Temporal variations in the statistical properties of the data resulting from changes in measurement and data storage methodologies were adjusted to match the statistical properties resulting from the data collection procedures that have been in place since January 1, 1989 (Over and others, 2010). The adjustments were computed based on the regressions between the primary data series from ANL and the backup series using data obtained during common periods; the statistical properties of the regressions were used to assign estimated standard errors to values that were adjusted or filled from other series. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2020) station at St. Charles, Illinois is used as "backup" for the air temperature, solar radiation and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2020) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service from the station at O'Hare International Airport and used as "backup". Each data source flag is of the form "xyz" that allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2020, Meteorological data, accessed on November 17, 2020, at http://gonzalo.er.anl.gov/ANLMET/. Bera, M., 2020, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9X0P4HZ. Midwestern Regional Climate Center, 2020, Meteorological data, accessed on November 3, 2020, at https://mrcc.illinois.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2020. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on November 9, 2020, at http://dx.doi.org/10.13012/J8MW2F2Q.

Chile INE Projection: Population: Metropolitan Santiago: San Ramon data was reported at 75.175 Person th in 2035. This records a decrease from the previous number of 75.960 Person th for 2034. Chile INE Projection: Population: Metropolitan Santiago: San Ramon data is updated yearly, averaging 86.548 Person th from Jun 2002 (Median) to 2035, with 34 observations. The data reached an all-time high of 100.574 Person th in 2002 and a record low of 75.175 Person th in 2035. Chile INE Projection: Population: Metropolitan Santiago: San Ramon data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.G002: Population: Projection.

Abstract

The FAO has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). The FAO launched a fifth-round household survey on 14 June 2021 in Iraq.

The sample of 1,354 agricultural households was derived from a list of farmers registered with the Ministry of Agriculture and is therefore not representative of the country’s entire population or agricultural population. Using computer-assisted telephone interviews, 385 households per governorate were interviewed across Ninewa, Duhok, Diyala and Maysan governorates, representing Iraq’s distinct agro-ecological regions. For more information, please go to https://data-in-emergencies.fao.org/pages/monitoring

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of 1,354 agricultural households was derived from a list of farmers registered with the Ministry of Agriculture and is therefore not representative of the country's entire population or agricultural population. Using computer-assisted telephone interviews, 385 households per governorate were interviewed across Ninewa, Duhok, Diyala and Maysan governorates, representing Iraq's distinct agro-ecological regions. Data collection coincided with the harvest season (which lasts from mid-April until July) and followed low rainfall during the planting season (October and November 2020) and drought in the growing season (February to April 2021). The survey also coincided with a rise in COVID-19 cases across the country - with a third wave of infections starting at the beginning of June and peaking at the end of July. Over 99 percent of the targeted sample was reached in all governorates except for Dohuk. Here, only 52 percent of the targeted sampled was interviewed because the team did not have contact information for all farmers.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

A link to the questionnaire has been provided in the documentations tab.

Cleaning operations

The datasets have been edited and processed for analysis by the Needs Assessment team at the Office of Emergency and Resilience, FAO, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries

Information on building organisations of private buildings in Hong Kong.

According to Cognitive Market Research, the global In-Memory Database market size will be 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.

Costa Rica CR: Population in Largest City: as % of Urban Population data was reported at 34.747 % in 2024. This records an increase from the previous number of 34.658 % for 2023. Costa Rica CR: Population in Largest City: as % of Urban Population data is updated yearly, averaging 46.499 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 51.171 % in 1963 and a record low of 34.420 % in 2020. Costa Rica CR: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

Data comprise stable element concentrations in terrestrial Reference Animals and Plants (RAPs) and corresponding whole-body concentration ratios determined in two different Mediterranean ecosystems: a Pinewood and a Dehesa (grassland with disperse tree cover). The International Commission on Radiological Protection (ICRP) RAPs considered in the Pinewood ecosystem were Pine Tree and Wild Grass; whereas in the Dehesa ecosystem those considered were Deer, Rat, Earthworm, Bee, Frog, Duck and Wild Grass. The data include: elemental concentrations in soils; elemental concentrations in plants, invertebrates, vertebrate tissues and estimated concentrations for vertebrate whole-organisms; individual concentration ratios (relating the fresh matter concentration in organisms to the dry matter concentration in soil); vertebrate species tissue masses; fresh to dry matter data for invertebrate species; geometric and arithmetic mean and standard deviation summaries for elemental concentrations and concentration ratios. Elemental concentrations presented include I, Li, Be, B, Na, Mg, Al, P, S, K. Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cd, Cs, Ba, Tl, Pb and U.

Yemen YE: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data was reported at 11.430 % in 2017. This records an increase from the previous number of 11.217 % for 2016. Yemen YE: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data is updated yearly, averaging 5.015 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 11.430 % in 2017 and a record low of 1.392 % in 1960. Yemen YE: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Yemen – Table YE.World Bank: Population and Urbanization Statistics. Population in urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2000 had a population of more than one million people.; ; United Nations, World Urbanization Prospects.; Weighted Average;

In January 2025, around ***** percent of Germany had 5G coverage. Only *** percent was a so-called dead zone, which is an area where there is no 2G, 4G, or 5G. The number of 5G base stations had increased significantly in recent years.

Florida COVID-19 Case Line data, exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu. Starting on 4/6/2021, the Florida Department of Health (FDOH) changed the way they provide COVID-19 caseline data. Beginning with this date the caseline data is being archived as two separate files, one for 2020 and one for 2021. The 2021 file will only include data from 1/1/2021 onward. In addition, FDOH has added two Object ID fields to their dataset. These caseline data are being preserved as they are provided by the FDOH, with a daily archive captured by the USF Libraries DHHC.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2021. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/. https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://www.arcgis.com/home/item.html?id=7a0c74a551904761812dc6b8bd620ee1 or Direct Download at: https://open-fdoh.hub.arcgis.com/datasets/7a0c74a551904761812dc6b8bd620ee1_0.

Archives for this data layer begin on 5/11/2020. Archived data was exported directly from the live FDOH layer into the archive by the University of South Florida Libraries - Digital Heritage and Humanities Collection.For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from the Florida Department of Health. This data table represents all laboratory-confirmed cases of COVID-19 in Florida tabulated from the previous day's totals by the Florida Department of Health. Persons Under Investigation/Surveillance (PUI):Essentially, PUIs are any person who has been or is waiting to be tested. This includes: persons who are considered high-risk for COVID-19 due to recent travel, contact with a known case, exhibiting symptoms of COVID-19 as determined by a healthcare professional, or some combination thereof. PUI’s also include people who meet laboratory testing criteria based on symptoms and exposure, as well as confirmed cases with positive test results. PUIs include any person who is or was being tested, including those with negative and pending results.All PUIs fit into one of three residency types:1. Florida residents tested in Florida2. Non-Florida residents tested in Florida 3. Florida residents tested outside of Florida Florida Residents Tested Elsewhere: The total number of Florida residents with positive COVID-19 test results who were tested outsideof Florida, and were not exposed/infectious in Florida. Non-Florida Residents Tested in Florida: The total number of people with positive COVID-19 test results who were tested, exposed, and/or infectious while in Florida, but are legal residents of another state.Table Guide for Records of Confirmed Positive Cases of COVID-19"County": The Florida county where the individual with COVID-19's case has been processed. "Jurisdiction" of the case:"FL resident" -- a resident of Florida"Non-FL resident" -- someone who resides outside of Florida "Travel_Related": Whether or not the positive case of COVID-19 is designated as related to recent travel by the individual. "No" -- Case designated as not being a risk related to recent travel"Unknown" -- Case designated where a travel-related designation has not yet been made."Yes" -- Case is designated as travel-related for a person who recently traveled overseas or to an area with community"Origin": Where the person likely contracted the virus before arriving / returning to Florida."EDvisit": Whether or not an individual who tested positive for coronavirus visited and was admitted to an Emergency Department related to health conditions surrounding COVID-19."No" -- Individual was not admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19"Unknown" -- It is unknown whether the individual was admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19"Yes" -- Individual was admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19“Hospitalized”: Whether or not a patient who receives a positive laboratory confirmed test for COVID-19 receives inpatient care at a hospital at any time during illness. These people may no longer be hospitalized. This information does not indicate that a COVID-19 positive person is currently hospitalized, only that they have been hospitalized for health conditions relating to COVID-19 at some point during their illness. "No" -- Individual was not admitted for inpatient care at a hospital at any time during illness "Unknown" -- It is unknown whether the individual was admitted for inpatient care at a hospital at any time during illness "Yes" -- Individual was admitted for inpatient care at a hospital at some point during the illness "Died": Whether or not the individual who tested positive for COVID-19 died as a result of health complications from the viral infection. "NA" -- Not applicable / resident has not died "Yes" -- Individual died of a health complication resulting from COVID-19 "Contact": Whether the person contracted COVID-19 from contact with current or previously confirmedcases."No" -- Case with no known contact with current or previously confirmed cases"Yes" -- Case with known contact with current or previously confirmed cases"Unknown" -- Case where contact with current or previous confirmedcases is not known or under investigation"Case_": The date the positive laboratory result was received in the Department of Health’s database system and became a “confirmed case.” This is not the date a person contracted the virus, became symptomatic, or was treated. Florida does not create a case or count suspected/probable cases in the case counts without a confirmed-positive lab result. "EventDate": When the individual reported likely first experiencing symptoms related to COVID-19. "ChartDate": Also the date the positive laboratory result for an individual was received in the Department ofHealth’s database system and became a recorded, “confirmed case” of COVID-19 in the state. Data definitions updated by the FDOH on 5/13/2020.

Montenegro Imports from Spain of Polyamides in primary forms was US$106 during 2015, according to the United Nations COMTRADE database on international trade. Montenegro Imports from Spain of Polyamides in primary forms - data, historical chart and statistics - was last updated on July of 2025.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the 2000 Census Voting Districts for Union County stored in the 2006 TIGER Second Edition dataset.

Netherlands Exports of polyamides in primary forms to Colombia was US$1.08 Million during 2024, according to the United Nations COMTRADE database on international trade. Netherlands Exports of polyamides in primary forms to Colombia - data, historical chart and statistics - was last updated on June of 2025.

Colombia Not in Labour Force: Manizales y Villa María data was reported at 149.956 Person th in Apr 2019. This records a decrease from the previous number of 151.944 Person th for Mar 2019. Colombia Not in Labour Force: Manizales y Villa María data is updated monthly, averaging 138.133 Person th from Mar 2001 (Median) to Apr 2019, with 218 observations. The data reached an all-time high of 152.668 Person th in Feb 2019 and a record low of 109.186 Person th in Mar 2002. Colombia Not in Labour Force: Manizales y Villa María data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.G008: Labour Force: Household Survey.

Database Security Solution Market Outlook

The global database security solution market was valued at USD 4.5 billion in 2023 and is projected to reach USD 11.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2032. This remarkable growth can be attributed to the increasing volume of data generated and stored by organizations, rising cyber threats, regulatory compliance requirements, and the growing adoption of cloud-based services across various industries.

One of the primary growth factors for the database security solution market is the exponential increase in data generation and storage. With the advent of big data, IoT, and advanced analytics, organizations are producing vast amounts of data that need to be securely stored and managed to prevent unauthorized access and data breaches. As a result, there is a growing demand for robust database security solutions that can protect sensitive information across diverse databases and platforms, ensuring data privacy and integrity.

Another significant growth driver is the rising number of cyber threats and data breaches. Organizations face sophisticated cyber-attacks that target confidential and high-value data, leading to financial losses, reputational damage, and regulatory penalties. This has necessitated the implementation of advanced database security solutions that offer real-time threat detection, encryption, access control, and audit capabilities to safeguard critical data and maintain business continuity.

Compliance with stringent regulatory frameworks is also propelling the growth of the database security solution market. Regulations such as GDPR, HIPAA, and CCPA mandate the protection of personal and sensitive information, compelling organizations to adopt comprehensive database security measures. Businesses are investing heavily in database security solutions to meet these regulatory requirements, avoid hefty fines, and build customer trust by ensuring data confidentiality and compliance.

The advent of Big Data Security has become a pivotal aspect in the realm of database security solutions. As organizations increasingly rely on big data analytics to drive business insights, the security of this data becomes paramount. Big Data Security involves implementing comprehensive measures to protect large volumes of data from unauthorized access and breaches. It encompasses various strategies, including encryption, access controls, and real-time monitoring, to ensure that sensitive data remains protected throughout its lifecycle. As the volume and complexity of data continue to grow, the demand for advanced Big Data Security solutions is expected to rise, driving further innovation and investment in this area.

Regionally, the database security solution market is witnessing significant growth, with North America leading the charge due to its advanced technological infrastructure, early adoption of innovative security solutions, and stringent data protection laws. Europe is also experiencing substantial growth driven by the enforcement of GDPR and increasing awareness of data privacy issues. The Asia Pacific region is projected to witness the highest CAGR during the forecast period, fueled by the rapid digital transformation, rising cyber threats, and growing government initiatives to enhance cybersecurity.

Component Analysis

The database security solution market can be segmented by component into software, hardware, and services. The software segment holds the largest market share, driven by the extensive use of database security software to protect data against unauthorized access, malware, and other cyber threats. These software solutions offer various functionalities such as encryption, access control, auditing, and monitoring, making them indispensable for organizations looking to secure their databases effectively.

The hardware segment, although smaller compared to software, plays a crucial role in enhancing database security. Hardware-based security solutions, such as hardware security modules (HSMs), are used for cryptographic key management and secure storage of sensitive data. These solutions provide an additional layer of security by ensuring that cryptographic operations are performed in a tamper-resistant environment, thus preventing unauthorized access and key compromise.

The services segment is also witnessing significant growth, driven by the increasing demand for m

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Address Ranges Relationship File (ADDR.dbf) contains the attributes of each address range. Each address range applies to a single edge and has a unique address range identifier (ARID) value. The edge to which an address range applies can be determined by linking the address range to the All Lines Shapefile (EDGES.shp) using the permanent topological edge identifier (TLID) attribute. Multiple address ranges can apply to the same edge since an edge can have multiple address ranges. Note that the most inclusive address range associated with each side of a street edge already appears in the All Lines Shapefile (EDGES.shp). The TIGER/Line Files contain potential address ranges, not individual addresses. The term "address range" refers to the collection of all possible structure numbers from the first structure number to the last structure number and all numbers of a specified parity in between along an edge side relative to the direction in which the edge is coded. The address ranges in the TIGER/Line Files are potential ranges that include the full range of possible structure numbers even though the actual structures may not exist.

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents landmark layer stored in the 2006 TIGER Second Edition dataset for Bernalillo County, NM.