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49-567: International Territorial Level ( ITL ) is a geocode standard for referencing the subdivisions of the United Kingdom for statistical purposes, used by the Office for National Statistics (ONS). From 2003 and until 2020 it functioned as part of the European Union and European Statistical System 's geocode standard Nomenclature of Territorial Units for Statistics or NUTS. Following Brexit ,

98-440: A clustered file system , using file systems that employ block level checksums such as ZFS , storage arrays that compute parity calculations such as exclusive or or use a cryptographic hash function and even having a watchdog timer on critical subsystems. Physical integrity often makes extensive use of error detecting algorithms known as error-correcting codes . Human-induced data integrity errors are often detected through

147-565: A country , is also a valid postal code. Not all postal codes are geographic, and for some postal code systems, there are codes that are not geocodes (e.g. in UK system ). Samples, not a complete list: Geocodes in use for telephony or radio broadcasting scope: Geocodes in use and with specific scope: Other geocodes: Some standards and name servers include: ISO 3166, FIPS, INSEE, Geonames, IATA and ICAO . A number of commercial solutions have also been proposed: Data integrity Data integrity

196-451: A hierarchical geocode system with same prefix represents different parts of the same location. For instance DE.NW.CE and DE.NW.BN represents geographically interior parts of DE.NW , the common prefix. Changing the subdivision criteria we can obtain other hierarchical systems. For example, for hydrological criteria there is a geocode system, the US's hydrologic unit code (HUC), that

245-458: A mosaic of subdivisions. Each subdivision can be partitioned again, recursively , resulting in an hierarchical mosaic. When subdivisions's names are expressed as codes, and code syntax can be decomposed into a parent-child relations, through a well-defined syntactic scheme, the geocode set configures a hierarchical system. A geocode fragment (associated to a subdivision name) can be an abbreviation, numeric or alphanumeric code. A popular example

294-516: A computer file system may be configured on a fault-tolerant RAID array, but might not provide block-level checksums to detect and prevent silent data corruption . As another example, a database management system might be compliant with the ACID properties, but the RAID controller or hard disk drive's internal write cache might not be. This type of integrity is concerned with the correctness or rationality of

343-413: A data value is derived based on algorithm, contributors and conditions. It also specifies the conditions on how the data value could be re-derived. Data integrity is normally enforced in a database system by a series of integrity constraints or rules. Three types of integrity constraints are an inherent part of the relational data model : entity integrity, referential integrity and domain integrity. If

392-587: A database supports these features, it is the responsibility of the database to ensure data integrity as well as the consistency model for the data storage and retrieval. If a database does not support these features, it is the responsibility of the applications to ensure data integrity while the database supports the consistency model for the data storage and retrieval. Having a single, well-controlled, and well-defined data-integrity system increases: Modern databases support these features (see Comparison of relational database management systems ), and it has become

441-462: A fine-grained schema, by longer path of keys. For example, the Geohash 6vd2 , which is a base32 code, can be expanded to base4 0312312002 , which is also a schema with per-digit keys. Geometrically, each Geohash cell is a rectangle that subdivides space recurrently into 32 new rectangles, so, base4 subdividing into 4, is the encoding-expansion limit. The uniformity of shape and area of cells in

490-475: A given location has not been assigned an address by authorities. They can also be used as an "alternative address" if it can be converted to a Geo URI . Even if the geocode is not the official designation for a location, it can be used as a "local standard" to allow homes to receive deliveries, access emergency services, register to vote, etc. Geocodes in use, as postal codes . A geocode recognized by Universal Postal Union and adopted as "official postal code" by

539-564: A grid can be important for other uses, like spatial statistics . There are standard ways to build a grid covering the entire globe with cells of equal area, regular shape and other properties: Discrete Global Grid System (DGGS) is a series of discrete global grids satisfying all standardized requirements defined in 2017 by the OGC . When human-readable codes obtained from cell identifiers of a DGGS are also standardized, it can be classified as DGGS based geocode system . There are also mixed systems, using

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588-504: A location . For example, for ISO, the country name “People's Republic of China” is a label. Geocodes are mainly used (in general as an atomic data type ) for labelling , data integrity , geotagging and spatial indexing . In theoretical computer science a geocode system is a locality-preserving hashing function . There are some common aspects of many geocodes (or geocode systems ) that can be used as classification criteria: The set of all geocodes used as unique identifiers of

637-409: A mixed reference convention, the system must be reversible. Pure name-and-grid systems, like Mapcode , with no way to transform it into a global code, is not a mixed reference, because there is no algorithm to transform the mixed geocode into a grid-based geocode. Geocodes in use and with general scope: Geocodes can be used in place of official street names and/or house numbers , particularly when

686-426: A new local grid, in a recurring process . In the illustrated example, the cell TQ 2980 is a sub-cell of TQ 29 , that is a sub-cell of TQ . A system of geographic regular grid references is the base of a hierarchical geocode system . Two geocodes of a hierarchical geocode grid system can use the prefix rule: geocodes with same prefix represents different parts of the same broader location . Using again

735-722: A piece of data, given a particular context. This includes topics such as referential integrity and entity integrity in a relational database or correctly ignoring impossible sensor data in robotic systems. These concerns involve ensuring that the data "makes sense" given its environment. Challenges include software bugs , design flaws, and human errors. Common methods of ensuring logical integrity include things such as check constraints , foreign key constraints , program assertions , and other run-time sanity checks. Physical and logical integrity often share many challenges such as human errors and design flaws, and both must appropriately deal with concurrent requests to record and retrieve data,

784-408: A proxy term for data quality , while data validation is a prerequisite for data integrity. Data integrity is the opposite of data corruption . The overall intent of any data integrity technique is the same: ensure data is recorded exactly as intended (such as a database correctly rejecting mutually exclusive possibilities). Moreover, upon later retrieval , ensure the data is the same as when it

833-626: A single or a group of two or three boroughs. Following Brexit, the classification used by the ONS was replaced with ITLs. Between 2021 and the next review scheduled for 2024, the ITLs are a mirror of the NUTS classification adopted in 2018. All NUTS codes containing "UK" were changed to use "TL" for Territorial Level . Below the ITL levels, the two LAU (Local Administrative Units) levels are: The two LAU levels are maintained by

882-419: A suitable sufficiently close locality. When the mixed reference is also short (9 characters in the second example) and there are a syntax convention to express it (suppose  CP‑PR~bgxed ), this convention is generating a new name-and-grid geocode system . This is not the case of the first example because, strictly speaking, "Cape Verde, Praia" is not a code. To be both, a name-and-grid system and also

931-433: A syntactical partition, where for example the first part (code prefix) is a name-code and the other part (code suffix) is a grid-code. Example: For mnemonic coherent semantics, in fine-grained geocode applications, the mixed solutions are most suitable. Any geocode system based on regular grid , in general is also a shorter way to express a latitudinal/longitudinal coordinate. But a geocode with more than 6 characters

980-409: A table of official names, and the corresponding official codes and geometries (typically polygon of administrative areas). "Official" in the context of control and consensus, typically a table controlled by a standards organization or governmental authority. So, the most general case is a table of standard names and the corresponding standard codes (and its official geometries). Strictly speaking,

1029-473: Is a numeric representation of basin names in a hierarchical syntax schema (first level illustred). For example, the HUC 17 is the identifier of " Pacific Northwest Columbia basin "; HUC 1706 of " Lower Snake basin ", a spatial subset of HUC 17 and a superset of 17060102 ("Imnaha River"). Inspired in the classic alphanumeric grids , a discrete global grid ( DGG ) is a regular mosaic which covers

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1078-416: Is also a geocoder . Sometimes names are translated into numeric codes, to be compact or machine-readable. Since numbers, in this case, are name identifiers, we can consider "numeric names" — so this set of codes will be a kind of "system of standard names". In the geocode context, space partitioning is the process of dividing a geographical space into two or more disjoint subsets , resulting in

1127-493: Is dated 21 November 2016 and was effective from 1 January 2018, listed 12 regions at NUTS 1, 40 regions at NUTS 2, and 174 regions at NUTS 3 level. The 12 ITL regions of the United Kingdom are listed below. Population numbers are for mid-2019 (as NUTS 1), and areas are in square kilometres. Data is from the Office for National Statistics . In the first version in 2003, North Eastern Scotland (which then included part of Moray)

1176-414: Is difficult for remember. On the other hand, a geocode based on standard name (or abbreviation or the complete name) is easier to remember. This suggests that a "mixed code" can solve the problem, reducing the number of characters when a name can be used as the "context" for the grid-based geocode. For example, in a book where the author says "all geocodes here are contextualized by the chapter's city". In

1225-609: Is the ISO 3166-2 geocode system, representing country names and the names of respective administrative subdivisions separated by hyphen. For example DE is Germany , a simple geocode, and its subdivisions (illustrated) are DE-BW for Baden-Württemberg , DE-BY for Bayern , ..., DE-NW for Nordrhein-Westfalen , etc. The scope is only the first level of the hierarchy. For more levels there are other conventions, like HASC code. The HASC codes are alphabetic and its fragments have constant length (2 letters). Examples: Two geocodes of

1274-403: Is the maintenance of, and the assurance of, data accuracy and consistency over its entire life-cycle . It is a critical aspect to the design, implementation, and usage of any system that stores, processes, or retrieves data. The term is broad in scope and may have widely different meanings depending on the specific context even under the same general umbrella of computing . It is at times used as

1323-441: The "name" related to a geocode is a toponym , and the table (e.g. toponym to standard code) is the resource for toponym resolution : is the relationship process , usually effectuated by a software agent, between a toponym and "an unambiguous spatial footprint of the same place". Any standardized system of toponym resolution, having codes or encoded abbreviations, can be used as geocode system . The "resolver" agent in this context

1372-548: The Greater London NUTS 1 area was left unchanged however the previous NUTS 2 area of inner and outer London were abolished and with the previous NUTS 3 areas becoming NUTS 2 areas. Thus NUTS 2 of Inner London West UKI11 becoming the NUTS 3 area of UKI3 and likewise: Inner London East (from UKI12 to UKI4), Outer London East and North East (from UKI21 to UKI5), Outer London South (from UKI22 to UKI6) and Outer London West and North West (from UKI23 to UKI7). The NUTS 3 areas are now

1421-470: The ONS set to develop a domestic statistical classification framework separate from NUTS. Currently, the ITLs are a mirror to the pre-existing NUTS system, they retain the same three level hierarchy and boundaries used for NUTS in the United Kingdom since 2018, with the next review scheduled for 2024. ITLs are set to follow a similar review timetable to NUTS, being reviewed every three years. The ONS will develop new official GSS codes of ITL geography aligned with

1470-527: The UK Office for National Statistics within the ONS coding system . The LAU codes of the United Kingdom can be downloaded here: [1] [REDACTED] Geocode Typical geocodes and entities represented by it: The ISO 19112:2019 standard (section 3.1.2) adopted the term "geographic identifier" instead geocode, to encompass long labels: spatial reference in the form of a label or code that identifies

1519-446: The case of OLC there is a second key schema, after the + separator: 58PJ642P+48 is the key 2 of the cell  58PJ642P+4 . It uses two key schemas. Some geocodes systems (e.g. S2 geometry) also use initial prefix with non-hierarchical key schema. In general, as technical and non-compact optional representation, geocode systems (based on hierarchical grids) also offer the possibility of expressing their cell identifier with

International Territorial Level - Misplaced Pages Continue

1568-425: The cell ID is standardized, it becomes a geocode. Geocodes of different geocode systems can represent the same position in the globe, with same shape and precision, but differ in string -length, digit-alphabet, separators, etc. Non-global grids also differ by scope, and in general are geometrically optimized (avoid overlaps, gaps or loss of uniformity) for the local use. Each cell of a grid can be transformed into

1617-415: The cells of a full-coverage of the geographic surface (or any well-defined area like a country or the oceans), is a geocode system (also named geocode scheme ). The syntax and semantic of the geocodes are also components of the system definition: Many syntax and semantic characteristics are also summarized by classification. Any geocode can be translated from a formal (and expanded) expression of

1666-484: The changes are the result of unauthorized access, it may also be a failure of data security. Depending on the data involved this could manifest itself as benign as a single pixel in an image appearing a different color than was originally recorded, to the loss of vacation pictures or a business-critical database, to even catastrophic loss of human life in a life-critical system . Physical integrity deals with challenges which are associated with correctly storing and fetching

1715-448: The chapter about Paris, where all places have a Geohash with prefix u09 , that code can be removed —. For instance Geohash u09tut can be reduced to tut , or, by an explicit code for context "FR-Paris tut ". This is only possible when the context resolution (e.g. translation from "FR-Paris" to the prefix u09 ) is well-known. In fact a methodology exists for hierarchical grid-based geocodes with non-variable size, where

1764-473: The code prefix describes a broader area, which can be associated with a name. So, it is possible to shorten by replacing the prefix to the associated context. The most usual context is an official name. Examples: The examples of the Mixed reference column are significantly easier than remembering DGG code column. The methods vary, for example OLC can be shortened by elimination of its first four digits and attaching

1813-496: The data itself. Challenges with physical integrity may include electromechanical faults, design flaws, material fatigue , corrosion , power outages , natural disasters, and other special environmental hazards such as ionizing radiation , extreme temperatures, pressures and g-forces . Ensuring physical integrity includes methods such as redundant hardware, an uninterruptible power supply , certain types of RAID arrays, radiation hardened chips, error-correcting memory , use of

1862-749: The database itself, which automatically ensures the accuracy and integrity of the data so that no child record can exist without a parent (also called being orphaned) and that no parent loses their child records. It also ensures that no parent record can be deleted while the parent record owns any child records. All of this is handled at the database level and does not require coding integrity checks into each application. Various research results show that neither widespread filesystems (including UFS , Ext , XFS , JFS and NTFS ) nor hardware RAID solutions provide sufficient protection against data integrity problems. Some filesystems (including Btrfs and ZFS ) provide internal data and metadata checksumming that

1911-405: The de facto responsibility of the database to ensure data integrity. Companies, and indeed many database systems, offer products and services to migrate legacy systems to modern databases. An example of a data-integrity mechanism is the parent-and-child relationship of related records. If a parent record owns one or more related child records all of the referential integrity processes are handled by

1960-423: The entire Earth's surface (the globe). The regularity of the mosaic is defined by the use of cells of same shape in all the grid, or "near the same shape and near same area" in a region of interest, like a country. All cells of the grid have an identifier (DGG's cell ID), and the center of the cell can be used as reference for cell ID conversion into geographical point. When a compact human-readable expression of

2009-461: The existing NUTS codes. From 1 January 2021, the ONS encourages "ITL" be used as a replacement to the "NUTS" designation, with lookups between NUTS and ITL maintained and published until 2023. The current ITL classification is a mirror of the previous NUTS classification with slight modification, the ONS lists 12 regions at ITL 1, 41 regions at ITL 2, and 179 regions at ITL 3. "UK" in the NUTS codes were replaced with "TL". The last NUTS classification

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2058-436: The geographical entity, or vice versa, the geocode translated to entity. The first is named encode process, the second decode . The actors and process involved, as defined by OGC , are: In spatial indexing applications the geocode can also be translated between human-readable (e.g. hexadecimal ) and internal (e.g. binary 64-bit unsigned integer ) representations. Geocodes like country codes , city codes, etc. comes from

2107-514: The latter of which is entirely a subject on its own. If a data sector only has a logical error, it can be reused by overwriting it with new data. In case of a physical error, the affected data sector is permanently unusable. Data integrity contains guidelines for data retention , specifying or guaranteeing the length of time data can be retained in a particular database (typically a relational database ). To achieve data integrity, these rules are consistently and routinely applied to all data entering

2156-455: The relations a piece of data can have to other pieces of data, such as a Customer record being allowed to link to purchased Products , but not to unrelated data such as Corporate Assets . Data integrity often includes checks and correction for invalid data, based on a fixed schema or a predefined set of rules. An example being textual data entered where a date-time value is required. Rules for data derivation are also applicable, specifying how

2205-437: The side illustration: TQ 28 and TQ 61 represents geographically interior parts of TQ , the common prefix. Hierarchical geocode can be split into keys. The Geohash 6vd23gq is the key q of the cell 6vd23g , that is a cell of 6vd23 (key g ), and so on, per-digit keys. The OLC 58PJ642P is the key 48 of the cell 58PJ64 , that is a cell of 58Q8 (key 48 ), and so on, two-digit keys. In

2254-446: The system, and any relaxation of enforcement could cause errors in the data. Implementing checks on the data as close as possible to the source of input (such as human data entry), causes less erroneous data to enter the system. Strict enforcement of data integrity rules results in lower error rates, and time saved troubleshooting and tracing erroneous data and the errors it causes to algorithms. Data integrity also includes rules defining

2303-508: The use of simpler checks and algorithms, such as the Damm algorithm or Luhn algorithm . These are used to maintain data integrity after manual transcription from one computer system to another by a human intermediary (e.g. credit card or bank routing numbers). Computer-induced transcription errors can be detected through hash functions . In production systems, these techniques are used together to ensure various degrees of data integrity. For example,

2352-686: Was coded UKM1, and Highlands and Islands was coded UKM4. The current NUTS level 1 codes start with "C" (following "UK") rather than "1" because the new list reflected the revised regions of England and local government changes throughout the UK; "1" to "B" had been used for the 11 regions in the previous coding system. NUTS 2006 came into force on 1 January 2008. NUTS 2010 came into force on 1 January 2012. 2010 changes to NUTS 2 also resulting in changes with NUTS 3 regions 2010 changes to NUTS 3 areas without changes occurring to NUTS 2 areas NUTS 2013 came into force on 1 January 2015. 2015 changes to NUTS 3 areas without changes to NUTS 2 areas: In 2015

2401-428: Was originally recorded. In short, data integrity aims to prevent unintentional changes to information. Data integrity is not to be confused with data security , the discipline of protecting data from unauthorized parties. Any unintended changes to data as the result of a storage, retrieval or processing operation, including malicious intent, unexpected hardware failure, and human error , is failure of data integrity. If

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