transit_stops
| completeness | red | The most critical completeness issue is the absence of a defined Coordinate Reference System (CRS) for this spatial dataset (no .prj file). While the internal pipeline handles reprojection, it cannot function without knowing the source CRS, rendering the spatial data unusable without manual intervention. Additionally, the 'OBJEKT' field appears to contain a single, generic value ('Zastávka MHD a IDP') across all sampled records, potentially indicating redundant information. |
| accuracy | red | The fundamental lack of a defined Coordinate Reference System (CRS) severely compromises the spatial accuracy of the transit stop locations. Without knowing the projection or datum, the geographic coordinates cannot be correctly interpreted or used, making the spatial component of this resource effectively inaccurate or unlocatable. This is a critical flaw for a spatial dataset like transit stops. |
| consistency | yellow | Based on the provided sample, the internal format of identifiers like 'ID_PMDP_GP' (e.g., 'XXX-Y') appears consistent. The 'OBJEKT' field consistently holds the same value across all samples. However, the absence of a defined CRS introduces potential for spatial inconsistencies if different parts of the dataset were created under varying, unknown coordinate systems. Without this critical metadata, the overall spatial consistency cannot be guaranteed. |
| timeliness | green | No date fields or last update information are provided within the dataset or context to assess its timeliness. However, there is no explicit information to suggest that the data is outdated or not current, warranting a 'green' score by default in the absence of contrary evidence. |
| uniqueness | green | The fields 'ID_ZAST' and 'ID_PMDP_GP' appear to serve as unique identifiers. In the provided sample, all values for both fields are distinct, suggesting that these fields likely provide sufficient uniqueness for individual transit stops within the dataset. |
| lineage | yellow | No information regarding the data's origin, collection methods, or any processing history (lineage) is provided. This lack of metadata makes it difficult to understand the data's reliability, potential biases, or the methods by which it was generated and maintained. |
| governance | yellow | There is no information available regarding the data's owner, responsible department, update frequency policy, or contact person for inquiries. This absence of governance metadata makes it challenging to understand the data's authoritative source, how it is maintained, or whom to contact for clarification or issue resolution. |
municipality_parts
| completeness | red | The critical issue is the undefined Coordinate Reference System (CRS) for this spatial dataset, as indicated by the missing .prj file. This renders the spatial data unusable without manual intervention to determine the correct projection, severely impacting the completeness of the spatial information. While character encoding issues like "Èást obce - areál" and "Køimice" are present in the source data, these are noted as being handled by the ingestion pipeline and thus do not contribute to the score. |
| accuracy | red | The accuracy of the spatial data is critically compromised because the Coordinate Reference System (CRS) is undefined. Without a specified CRS, the geographic coordinates cannot be correctly interpreted or used, making the spatial representation inaccurate. The textual data appears plausible, but the fundamental spatial accuracy is missing due to the unknown projection. |
| consistency | yellow | The data structure appears consistent with three fields (OBJEKT, NAZEV, KOD), and values within these fields follow a consistent pattern in the provided sample. The "OBJEKT" field consistently shows "Èást obce - areál", which may indicate a fixed type rather than an inconsistency. However, the unknown CRS introduces an inconsistency when attempting to integrate this spatial data with other georeferenced datasets, as its spatial context cannot be consistently aligned. |
| timeliness | yellow | There is no information available within the provided dataset or metadata (such as update dates or effective dates) to assess the timeliness of these municipality parts. Therefore, it is impossible to determine if the records are current or outdated. |
| uniqueness | green | Based on the provided sample, the 'KOD' field appears to serve as a unique identifier for each municipality part, with all sample values being distinct. The 'NAZEV' field also shows unique values in the sample. There is no indication of duplicate records within the limited sample provided. |
| lineage | red | No information regarding the data's lineage is available, including its source, collection methodology, any transformations applied, or the date of creation/last update. This lack of lineage makes it impossible to understand the data's provenance and potential quality implications. |
| governance | red | There is no explicit information provided regarding data governance, such as the data owner, responsible department, update frequency, or associated data quality policies. The critical issue of an undefined Coordinate Reference System (CRS) also suggests a lack of enforced data standards or quality control processes at the point of data publication. |
municipal_districts
| completeness | red | The spatial data is critically incomplete as the Coordinate Reference System (CRS) is undefined due to the absence of a .prj file. This prevents proper spatial interpretation and use of the geometry, rendering the dataset largely unusable for spatial analysis, despite the count of records matching the total expected. |
| accuracy | red | The accuracy of the spatial data is critically compromised as the Coordinate Reference System (CRS) is unknown. Without a defined CRS, the geographic location of the municipal districts cannot be accurately determined or used, making the spatial component of the data effectively inaccurate and unsuitable for precise mapping or analysis. |
| consistency | yellow | While the data values within the fields appear internally consistent (e.g., 'OBJEKT' always 'Mìstská èást - areál'), the absence of a defined Coordinate Reference System (CRS) via a .prj file is a major inconsistency with standard spatial data practices. This lack of a standard spatial reference hinders interoperability and reliable use across different systems. |
| timeliness | yellow | No information regarding the creation date or last update of the dataset is available, making it impossible to assess its timeliness or determine if the data accurately represents the current administrative boundaries of the municipal districts. |
| uniqueness | green | Based on the provided sample, the 'KOD' field appears to serve as a unique identifier for each municipal district, with distinct values observed across all records. The total number of records matches the sample size, indicating no explicit duplicates within the dataset. |
| lineage | yellow | No information regarding the data's origin, collection methods, or any transformation processes is provided. This absence of lineage metadata limits understanding of the data's history, potential biases, or any limitations that might affect its interpretation and use. |
| governance | yellow | Information regarding data ownership, update cycles, quality control procedures, or contact points for data stewards is not available. This lack of transparency makes it difficult to assess the underlying data governance framework and the reliability of ongoing data management practices. |
greenways
| completeness | red | The dataset is severely incomplete. It lacks a defined Coordinate Reference System (.prj file), rendering the spatial data unusable without manual intervention and assumptions. Furthermore, the single attribute field 'OBJEKT' contains only a generic, uninformative value ("Greenways - stav") across all sampled records, indicating a critical absence of meaningful descriptive data for the greenways. |
| accuracy | red | The accuracy is critically compromised by the undefined Coordinate Reference System. Without a .prj file, the spatial coordinates cannot be correctly interpreted or reprojected, making the spatial data effectively inaccurate and unsuitable for any precise analysis or mapping. The lack of specific attribute data prevents assessing attribute accuracy. |
| consistency | yellow | The dataset exhibits structural inconsistency due to the missing .prj file, which is a standard component for shapefiles to define their spatial reference. While the single 'OBJEKT' field is internally consistent in its uninformative value, this consistency does not add value and highlights a lack of meaningful attributes rather than internal data inconsistencies. |
| timeliness | yellow | No information regarding the creation or last update date of the dataset is provided, making it impossible to assess its timeliness. Without this crucial metadata, it's unknown whether the data reflects the current state of greenways in Pilsen. |
| uniqueness | red | The dataset critically lacks uniqueness in its attribute data. The sole attribute 'OBJEKT' contains the identical, generic value "Greenways - stav" for all sampled records, making it impossible to distinguish between individual greenway features based on their descriptive properties. |
| lineage | red | There is no information provided regarding the data source, the methodology used for data collection, any transformations applied, or the history of the dataset. This complete absence of lineage metadata makes it impossible to understand the data's origin and reliability. |
| governance | red | The absence of a defined CRS, the lack of meaningful attributes, and the general scarcity of metadata (like creation date or source) strongly suggest poor data governance practices. There is no indication of ownership, update policies, or quality control measures for this dataset. |
libraries
| completeness | red | The dataset contains all 46 records as indicated. While all specified fields appear populated in the sample, a critical completeness issue exists because the Coordinate Reference System (CRS) is not defined. Without a specified CRS, the spatial coordinates are effectively incomplete and cannot be reliably used or integrated, rendering the spatial aspect of the data unusable. |
| accuracy | red | The most significant accuracy concern is the 'not defined' Coordinate Reference System (CRS). This means the spatial coordinates of the library locations are uninterpretable, making it impossible to accurately place these points on a map or perform spatial analysis. While the textual attributes in the sample appear plausible, the fundamental spatial accuracy is critically compromised by the missing CRS. |
| consistency | red | The dataset exhibits structural consistency with all records having the same fields, and the categorical values in the 'TYP' field appear consistent within the sample. However, the absence of a defined Coordinate Reference System (CRS) presents a critical consistency issue for spatial data. Without a known CRS, the spatial information cannot be consistently interpreted or integrated with other spatial datasets, leading to potential misalignments and errors. |
| timeliness | yellow | There is no information provided regarding when this data was last updated or collected, nor are there any date-related fields within the dataset itself. This lack of update frequency or last-modified information makes it difficult to assess the current timeliness of the library locations and their associated details. |
| uniqueness | yellow | The dataset does not appear to contain a dedicated unique identifier for each library record. While combinations of fields like 'NAZEV' and 'ADRESA' might serve as de facto identifiers in some cases, the absence of an explicit, guaranteed unique ID can complicate data integration, updates, and referencing individual records unambiguously. |
| lineage | red | No information is provided regarding the origin, creation process, or any transformations applied to this dataset. Understanding the data's lineage is crucial for assessing its trustworthiness and understanding potential biases or limitations, and its complete absence here is a significant drawback. |
| governance | red | There is no information available regarding the data owner, the entity responsible for its maintenance, the defined update schedule, or any data quality policies. A lack of clear data governance makes it challenging to understand accountability, ensure ongoing quality, and plan for future data sustainability. |
land_use_zones
| completeness | red | The dataset is critically incomplete due to the absence of a defined Coordinate Reference System (CRS). The lack of a '.prj' file means essential spatial metadata is missing, preventing the data from being accurately integrated or used within a geospatial context, even with internal re-projection capabilities that rely on a known source CRS. |
| accuracy | red | The spatial accuracy of the land use zones is severely compromised by the undefined Coordinate Reference System. Without knowledge of the original projection, the geographic coordinates cannot be correctly interpreted or transformed, making any spatial analysis or mapping based on this data unreliable and potentially erroneous. |
| consistency | green | The provided sample records suggest good internal consistency within the dataset. Field values for 'VYUZITI' appear uniformly formatted, and the overall data structure seems coherent, indicating a consistent approach to data recording. |
| timeliness | yellow | No temporal information, such as creation date, last update, or validity period, is available for this dataset. This absence makes it impossible to determine the current relevance or timeliness of the land use zone data for contemporary urban planning and research. |
| uniqueness | yellow | The dataset lacks an explicit unique identifier for each land use zone record. While combinations of existing fields like 'KOD' and 'NAZEV' might offer some distinction, there is no guarantee of global uniqueness, which could complicate data management, updates, and integration with other systems. |
| lineage | red | There is a complete lack of lineage information, including details about the data's source, collection methodology, processing steps, or any transformations applied. This omission makes it impossible to assess the data's origin, reliability, or suitability for specific applications. |
| governance | red | Critical information regarding data governance, such as the data owner, update frequency, quality control procedures, or a contact for inquiries, is entirely missing. This absence undermines trust in the data and the ability to address issues or understand its authoritative source. |
heritage_monuments
| completeness | red | The dataset is severely incomplete. It contains only a single attribute, 'OBJEKT', which describes the status or type of the monument rather than its identity. Critical information such as unique identifiers, monument names, addresses, historical details, designation dates, or protection levels are entirely missing, severely limiting the data's utility. |
| accuracy | red | A critical accuracy issue is the undefined Coordinate Reference System (CRS). The absence of a .prj file means the spatial reference of the data is unknown. Without knowing the source CRS, the geographic coordinates are fundamentally unusable or highly suspect, compromising the spatial accuracy of the entire dataset. |
| consistency | yellow | The primary consistency concern is the undefined CRS, which prevents consistent spatial interpretation and integration with other datasets. While the single 'OBJEKT' field appears internally consistent in its values (Czech terms), the overall lack of attributes and unique identifiers makes it difficult to ensure consistency across different data updates or external data sources. |
| timeliness | red | The dataset lacks any date-related attributes, such as creation date, last update date, or monument designation date. Consequently, it is impossible to assess the currency or timeliness of the heritage monument information provided. |
| uniqueness | red | There is no unique identifier field within the dataset. The 'OBJEKT' field is not unique, as demonstrated by the sample records containing repeated values. This absence makes it impossible to distinguish individual heritage monuments, track their status over time, or effectively manage them as distinct entities. |
| lineage | red | No information regarding the data's origin, creation methodology, update frequency, or any processing steps is provided. This complete lack of lineage metadata prevents any assessment of the data's reliability, trustworthiness, or how it has evolved. |
| governance | red | The fundamental issue of an undefined CRS (missing .prj file) and the extreme sparsity of attributes (only one non-identifier field) are strong indicators of poor data governance practices. This suggests a lack of established standards for data collection, documentation, and quality control. |
artworks
| completeness | red | The most critical completeness issue is the absence of a Coordinate Reference System (CRS) definition (no .prj file). For a spatial dataset like a shapefile, this renders the spatial information fundamentally incomplete and unusable without external knowledge or manual determination of the correct CRS. While the attribute fields appear to be present and populated in the sample, the core spatial component is critically lacking in definition. |
| accuracy | red | The lack of a defined Coordinate Reference System (CRS) directly impacts the spatial accuracy of the dataset. Without knowing the projection and datum, the geographic coordinates embedded within the shapefile cannot be accurately interpreted, transformed, or used for precise spatial analysis and mapping, making the spatial location information inherently inaccurate or unknown. Character encoding issues observed in the sample are noted but not penalized as per assessment context. |
| consistency | red | The absence of a .prj file for a shapefile represents a significant structural inconsistency for a spatial data format, as it is standard practice for shapefiles to include this spatial reference information. While the attribute data types and the use of 'KOD' as an identifier appear consistent within the sample, this fundamental structural inconsistency concerning the spatial component is critical. |
| timeliness | yellow | The dataset lacks any date-related fields or metadata that would allow for an assessment of its currency or update frequency. Without this information, it is impossible to determine if the artwork records are up-to-date, making timeliness an unassessable dimension. |
| uniqueness | green | The 'KOD' field appears to serve as a meaningful and unique identifier for each artwork in the provided sample. This suggests good practice in assigning distinct identifiers to individual records, which is crucial for data management and referencing. |
| lineage | yellow | No information regarding the data's origin, collection methods, last update date, or any processing steps is provided. This lack of lineage metadata makes it challenging to understand the data's history, reliability, and potential biases or limitations. |
| governance | yellow | The assessment lacks information about the data owner, update policies, quality control procedures, or contact points for data inquiries. This absence of governance metadata hinders transparency and accountability regarding the dataset's maintenance and quality assurance. |
trees
| completeness | red | The dataset is critically incomplete as the Coordinate Reference System (CRS) is undefined, rendering the spatial data unusable for accurate mapping or analysis. Furthermore, the dataset contains only two highly generic fields ('RC' and 'ID_STR'), providing minimal informational completeness for a 'trees' resource and lacking essential attributes like species, age, or health status. |
| accuracy | red | The accuracy of the spatial data is fundamentally compromised because the Coordinate Reference System (CRS) is unknown. Without a defined CRS, the geographic locations of the trees cannot be interpreted or used correctly, making the spatial data inaccurate by definition. |
| consistency | yellow | While the sample records show internal consistency in data types and values for the provided fields, the absence of a '.prj' file for a Shapefile indicates a structural inconsistency. A '.prj' file is a standard component for defining the CRS of a shapefile, and its omission makes the dataset structurally incomplete and difficult to process reliably. |
| timeliness | yellow | The dataset lacks any attributes or metadata indicating when the tree data was collected, last updated, or its general currency. Without this information, it is impossible to assess the timeliness of the records or determine if they represent the current state of trees in Pilsen. |
| uniqueness | green | The 'ID_STR' field appears to serve as a unique identifier for each record, with distinct values observed in the sample. Assuming this uniqueness holds true for the entire dataset, this field effectively distinguishes individual tree records. |
| lineage | red | There is a complete absence of lineage information, including details on the data's origin, collection methods, processing history, or any transformations applied. This lack of context makes it impossible to understand the data's provenance or assess its reliability over time. |
| governance | red | The critical omission of a defined Coordinate Reference System (CRS) for spatial data, along with the complete lack of metadata regarding data ownership, quality standards, or maintenance protocols, indicates significant deficiencies in data governance practices for this resource. |
parking_zones
| completeness | yellow | The dataset is missing crucial metadata for a spatial resource, specifically the Coordinate Reference System (CRS) definition, which renders its spatial component ambiguous. While the record count (339) is provided, the resource only contains two fields (`OBJEKT`, `NAZEV`), which may indicate a lack of comprehensive attributes typically expected for parking zones (e.g., capacity, operating hours, restrictions, pricing). |
| accuracy | red | The complete absence of a defined Coordinate Reference System (CRS) in the shapefile (indicated by the missing `.prj` file) is a critical accuracy concern for a spatial dataset. Without knowing the source CRS, any spatial analysis, reprojection, or display of the parking zones will be fundamentally inaccurate or impossible, as the geometric coordinates cannot be correctly interpreted or positioned. |
| consistency | yellow | The `OBJEKT` field consistently contains the value "Parkovací zóna" across all sample records, which, while internally consistent, suggests a potential lack of internal variation or meaningful classification within that field. More broadly, the missing CRS introduces a fundamental inconsistency for a spatial dataset, as its geometric interpretation and spatial relationships cannot be consistently understood or validated. |
| timeliness | yellow | No temporal information, such as creation or last update dates, is provided within the dataset or its metadata. This makes it impossible to ascertain the currentness or validity of the parking zone data, which can change over time due to urban planning decisions or infrastructure updates. |
| uniqueness | yellow | The dataset lacks a unique identifier for individual parking zone records. While `NAZEV` values (e.g., "Roudná", "Vyhrazené stání") can legitimately repeat for different zones, the absence of a distinct ID makes it challenging to uniquely reference, track, or integrate specific parking zones across systems or over time. |
| lineage | yellow | There is no information available regarding the origin, creation process, last modification, or any transformations applied to this dataset. This absence of lineage metadata hinders trust and understanding of the data's history, source, and reliability. |
| governance | yellow | No metadata is provided concerning the data owner, update frequency, quality assurance processes, or contact information for this dataset. This lack of governance information makes it difficult to understand who is responsible for the data, how it is maintained, and what policies guide its quality and distribution. |
parking_areas
| completeness | red | The most critical completeness issue is the absence of a defined Coordinate Reference System (.prj file), rendering the spatial data unusable without manual intervention and assumptions. Furthermore, the attribute data appears highly generic, with all sample records categorized as 'Parkovištì' of type 'ostatní', suggesting a lack of detailed information crucial for urban planning or user applications. |
| accuracy | red | The unknown Coordinate Reference System fundamentally compromises the spatial accuracy of the dataset; without it, the geographic location of the parking areas cannot be reliably determined. The uniformly generic attribute values ('Parkovištì', 'ostatní') in the sample also suggest a potential lack of precision or specificity in the descriptive data, raising concerns about attribute accuracy. |
| consistency | yellow | While the data structure with two fields appears internally consistent, the lack of a defined Coordinate Reference System introduces a significant inconsistency, preventing reliable spatial alignment with other datasets. The uniform and generic nature of the sample attribute values also suggests a potential lack of fine-grained classification consistency across different parking area types. |
| timeliness | yellow | No date information (e.g., creation date, last updated) is available for this resource, making it impossible to assess its currency or timeliness. Without this metadata, users cannot determine if the data reflects the current state of parking areas in Pilsen. |
| uniqueness | yellow | The dataset lacks an explicit unique identifier for individual parking areas, which is crucial for tracking, referencing, and updating records. The identical nature of the sample records ('Parkovištì', 'ostatní') further highlights a potential absence of distinguishing attributes that could uniquely identify each parking area. |
| lineage | red | There is no information provided regarding the data source, creation methodology, update frequency, or any transformations applied to this dataset. This complete absence of lineage metadata makes it impossible to understand the data's origin, reliability, or how it was produced. |
| governance | red | No information is available regarding data ownership, maintenance policies, data quality standards, or points of contact for this resource. This lack of governance information hinders trust, accountability, and the ability to address potential data issues or request updates. |
natural_parks
| completeness | red | The dataset contains only one record, which is suspiciously low for a city's natural parks. More critically, the Coordinate Reference System (CRS) is undefined due to a missing .prj file. This renders the spatial information unusable and effectively incomplete, as the geographical location of the park cannot be determined or reprojected accurately by the pipeline. |
| accuracy | red | The accuracy of this dataset is critically compromised by the absence of a defined Coordinate Reference System (CRS). Without a .prj file, the spatial coordinates provided for the natural park cannot be correctly interpreted or located on a map, rendering the spatial data entirely inaccurate and unusable for geographical analysis or display. |
| consistency | green | With only a single record present in the dataset, it is difficult to thoroughly assess internal consistency patterns. However, for the existing record, the 'OBJEKT' and 'NAZEV' fields appear to be logically consistent with each other, describing a natural park boundary and its name. |
| timeliness | yellow | The dataset lacks any explicit metadata regarding its creation date or last update. Without this information, it is impossible to determine the timeliness or currency of the natural park boundary data. While natural park definitions may not change frequently, the absence of date information hinders proper data lifecycle management. |
| uniqueness | yellow | While the single record present is inherently unique within this specific dataset, there is no dedicated unique identifier field (like a UUID or ID number). Relying solely on descriptive names such as 'Pøírodní park Berounka' can be problematic for data integration, updates, or ensuring uniqueness across larger or federated datasets if names are not guaranteed to be globally unique or stable. |
| lineage | red | There is no information provided regarding the data's origin, collection methodology, processing steps, or responsible agency. The complete absence of lineage metadata makes it impossible to understand the data's credibility, reliability, or how it has evolved over time, hindering trust and potential reuse. |
| governance | red | Critical governance information is entirely missing. There is no indication of the data owner, the frequency of updates, established data quality standards, or contact points for inquiries. This lack of governance metadata severely impedes proper data management, accountability, and user confidence in the resource. |
recreational_areas
| completeness | red | The dataset suffers from a critical completeness issue due to the undefined Coordinate Reference System (CRS). Without a .prj file or explicit CRS metadata, the spatial data is effectively unusable and incomplete, as its geographic context cannot be established. While all records have values for the 'OBJEKT' and 'NAZEV' fields, the fundamental lack of spatial reference renders the core purpose of a 'recreational_areas' dataset unfulfilled. |
| accuracy | red | The accuracy of the spatial information is critically compromised by the absence of a defined Coordinate Reference System (CRS). Without knowing the projection and datum, the geographic coordinates of the recreational areas cannot be accurately interpreted or positioned in the real world, rendering the spatial data unreliable and inaccurate for any practical application requiring precise location information. |
| consistency | yellow | The textual fields, 'OBJEKT' and 'NAZEV', show internal consistency, with 'OBJEKT' uniformly indicating "Rekreaèní oblast" and 'NAZEV' providing distinct names for each record. However, the lack of a defined Coordinate Reference System (CRS) introduces a significant structural inconsistency for a spatial dataset, preventing its consistent use and integration with other georeferenced data layers. |
| timeliness | green | There is no date information provided within the dataset or its metadata to assess its timeliness. Without any indicators of creation or last modification dates, it is assumed that the data is current and reflects the present state of recreational areas in Pilsen. However, this cannot be definitively confirmed. |
| uniqueness | green | Based on the provided sample, all 12 records are unique, with each 'NAZEV' field containing a distinct name for a recreational area. There are no apparent duplicate entries within the dataset, indicating good uniqueness for the primary identifiers. |
| lineage | red | There is a complete absence of lineage information for this dataset. Details regarding its origin, creation methodology, data sources, or any transformations applied are not provided. This lack of metadata makes it impossible to understand the data's history, assess its reliability, or trace potential errors back to their source. |
| governance | red | No information is available regarding the data's ownership, responsible department, update frequency, or quality assurance processes. The lack of clear data governance details makes it challenging to understand who is accountable for the dataset's quality, maintenance, and future updates, hindering long-term usability and trust. |
recreational_regulation
| completeness | red | The dataset provides 158 records with two textual fields. However, a critical gap exists in the spatial metadata: the Coordinate Reference System (CRS) is undefined due to a missing .prj file. For a spatial dataset, this renders the geometric information incomplete and effectively unusable without manual intervention to identify the correct spatial reference, severely impacting the utility of the resource. |
| accuracy | red | The primary accuracy concern stems directly from the undefined Coordinate Reference System (CRS). Without a known CRS, the precise geographic location and extent of the recreational regulation geometries cannot be verified or relied upon. This fundamental lack of spatial reference means the positional accuracy of the data is entirely compromised, making the dataset geographically inaccurate by default. |
| consistency | red | While the textual fields 'OBJEKT' and 'POPIS' appear internally consistent in their structure and content (e.g., 'OBJEKT' is uniformly 'Rekreace a prostorová regulace'), the absence of a defined Coordinate Reference System (CRS) introduces a critical inconsistency for spatial data. Without a clear and documented spatial reference, the geometric features cannot be consistently interpreted, aligned, or integrated with other spatial datasets, leading to potential misinterpretations and spatial discrepancies. |
| timeliness | yellow | No information regarding the creation date, last update, or validity period of the recreational regulations is available within the provided metadata or sample data. Without these details, it is impossible to assess the current relevance and timeliness of the dataset, which is crucial for urban planning and regulation enforcement. |
| uniqueness | yellow | The dataset does not appear to contain an explicit unique identifier for each regulation record. While spatial features implicitly have unique geometries, a clear alphanumeric ID would greatly facilitate tracking, referencing, and managing individual regulations, especially when performing updates or linking to other datasets. |
| lineage | yellow | No lineage information, such as the original data source, creation methodology, any transformations applied, or last modification details, is provided with this dataset. This absence makes it difficult to understand the data's provenance, assess its reliability over time, or trace any potential errors back to their origin. |
| governance | yellow | Information regarding data ownership, the responsible department for its maintenance, update frequency, or contact persons for inquiries is missing. This absence makes it challenging to understand the data's governance structure, support channels, and who to contact for clarification or reporting issues. |
offices_institutions
| completeness | red | The most critical completeness issue is the lack of a defined Coordinate Reference System (CRS) for this shapefile, as indicated by the absence of a `.prj` file. This renders the spatial data uninterpretable without external knowledge, significantly hindering its utility. Additionally, one sample record shows a missing value for the 'CINNOST' field, suggesting potential attribute incompleteness across the dataset. |
| accuracy | red | The absence of a defined Coordinate Reference System (CRS) critically undermines the spatial accuracy of the data, making it impossible to correctly geolocate the offices and institutions. Furthermore, the 'CINNOST' field, which is intended for activity descriptions, contains geographical locations (e.g., "Køimice", "Plzeò - Køimice") for some 'Èeská pota' entries. This represents a significant semantic inaccuracy, as the data in this field does not align with its intended meaning. |
| consistency | red | The data exhibits significant consistency issues. The lack of a defined CRS prevents any assessment of spatial consistency across features. More critically, the 'CINNOST' field is used inconsistently, sometimes containing an activity description, other times a geographical location, and occasionally being null. This violates data schema consistency and makes the field's content unreliable. The absence of a unique identifier for each record also represents a structural inconsistency, hindering reliable data management and linkage. |
| timeliness | yellow | No information regarding the data's collection date, last update, or validity period is provided within the dataset or its metadata. Without these details, it is impossible to assess the timeliness of the 'offices_institutions' resource, leaving its current relevance and up-to-dateness uncertain. |
| uniqueness | yellow | The dataset lacks an explicit unique identifier for each office or institution record. While the combination of 'NAZEV' and 'CINNOST' might serve as a de facto identifier in some cases, its uniqueness is not guaranteed or enforced without a dedicated field. This absence makes it challenging to identify and manage individual entities, and there is a potential for duplicate records if not carefully managed. |
| lineage | red | The dataset provides no information regarding its lineage, including data sources, collection methods, last update date, or any transformations applied. The absence of a defined Coordinate Reference System (CRS) further exemplifies poor lineage tracking, as the spatial origin of the data is unknown. This lack of transparency makes it impossible to understand the data's history, assess its reliability, or trace potential errors. |
| governance | red | The presence of critical data quality issues, such as the undefined Coordinate Reference System, inconsistent field usage, and missing values, strongly suggests a lack of robust data governance. There is no information provided about data ownership, responsible entities, update policies, or quality assurance processes. This indicates that formal data governance structures and practices are either absent or ineffective for this resource. |