The November 2021 release of ArcGIS Data Reviewer has new capabilities that automate and simplify the quality control of GIS data. This includes an expanded library of data validation checks to detect common errors in your data and enhanced workflows for managing errors detected during review. This unified set of capabilities to detect, manage, and report errors in your data enable you to lower data management costs while reducing risks in decision-making.<\/p>\n
Data Reviewer provides a library of checks that automate the detection of common errors in GIS data. Automated checks assess different aspects of a feature\u2019s quality that might include its attribution, spatial relationship with other features, or its integrity. Reviewer\u2019s checks are simple to use since they are configurable and do not require specialized skills to implement beyond a good understanding of your quality requirements.<\/p>\n
In the latest release, there are thirteen (13) new Reviewer checks that identify poor quality features in your data. A complete list of these checks can be found in the What\u2019s new in ArcGIS Pro 2.9<\/a> topic.<\/p>\n The latest release includes a series of checks that enable you to assess the quality of a feature\u2019s attribution in different ways. This includes identifying field values that are not unique (Unique Field Value<\/a> check) and cardinality and relationship rule violations in data sources that participate in a Relationship class (Relationship<\/a> check).<\/p>\n The Regular Expression<\/a> check finds features or rows containing text values that do not match an expected pattern. For example, in U.S. roadway data management, road centerline features contain a globally unique identifier (GUID) attribute that is both unique in value and formatted based on a national standard. Values in this field include a locally assigned ID and a pre-defined agency identify code (example:\u00a0 unique_value@mycounty.mystate.us). Using the Regular Expression<\/strong> check, values which do not match this formatting pattern are returned as an error.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1403282,"id":1403282,"title":"Image 1 - Regular Expression check result","filename":"Image-1-Regular-Expression-check-result.png","filesize":486696,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/data-reviewer\/data-management\/whats-new-in-arcgis-data-reviewer-november-2021\/image-1-regular-expression-check-result","alt":"Identify text values that do not meet a defined pattern.","author":"288792","description":"Data Reviewer error result from Regular Expression check","caption":"Identify text values that do not meet a defined pattern.","name":"image-1-regular-expression-check-result","status":"inherit","uploaded_to":1401942,"date":"2021-11-18 00:05:34","modified":"2021-11-18 00:06:25","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1568,"height":1076,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result.png","medium-width":380,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result.png","medium_large-width":768,"medium_large-height":527,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result.png","large-width":1568,"large-height":1076,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result-1536x1054.png","1536x1536-width":1536,"1536x1536-height":1054,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result.png","2048x2048-width":1568,"2048x2048-height":1076,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result-678x465.png","card_image-width":678,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-1-Regular-Expression-check-result.png","wide_image-width":1568,"wide_image-height":1076}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Other new checks have been added to assess spatial relationships between multiple data sources. Frequently-used checks in this category include the Polygon Gap is Sliver<\/a> and Composite<\/a> checks. The first one helps to identify gaps between two or more polygon features based on the shape and size of the gap area. This can be particularly helpful when evaluating the quality of features created using non-Esri applications or those created without a proper snapping environment.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1401962,"id":1401962,"title":"Image 2 - Polygon Gap is Sliver error","filename":"Image-2-Polygon-Gap-is-Sliver-error.png","filesize":59959,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/data-reviewer\/data-management\/whats-new-in-arcgis-data-reviewer-november-2021\/image-2-polygon-gap-is-sliver-error","alt":"Find sliver gaps between two or more polygon features","author":"288792","description":"","caption":"Find sliver gaps between two or more polygon features","name":"image-2-polygon-gap-is-sliver-error","status":"inherit","uploaded_to":1401942,"date":"2021-11-16 22:45:19","modified":"2021-11-16 22:46:00","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":569,"height":430,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error.png","medium-width":345,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error.png","medium_large-width":569,"medium_large-height":430,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error.png","large-width":569,"large-height":430,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error.png","1536x1536-width":569,"1536x1536-height":430,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error.png","2048x2048-width":569,"2048x2048-height":430,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error.png","card_image-width":569,"card_image-height":430,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-2-Polygon-Gap-is-Sliver-error.png","wide_image-width":569,"wide_image-height":430}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" The Composite<\/strong> check finds features and rows that fail evaluation by two or more Reviewer checks. This can be particularly useful when error results from a single check do not completely meet a quality requirement. An example of this applies to mapping topographic features. When mapping a tunnel opening point it must be coincident with either the end of a road, railway, aqueduct, or canal feature. By combining the results from four (4) Feature on Feature<\/a> checks, those tunnel openings which are not touching any of the above features are returned as an error.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1401992,"id":1401992,"title":"Image 3 - CompositeCheck","filename":"Image-3-CompositeCheck.png","filesize":51775,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/data-reviewer\/data-management\/whats-new-in-arcgis-data-reviewer-november-2021\/image-3-compositecheck","alt":"Combine multiple Reviewer checks to identify errors","author":"288792","description":"ArcGIS Data Reviewer Composite check","caption":"Combine multiple Reviewer checks to identify errors","name":"image-3-compositecheck","status":"inherit","uploaded_to":1401942,"date":"2021-11-16 22:50:00","modified":"2021-11-16 22:56:59","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":745,"height":466,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck.png","medium-width":417,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck.png","medium_large-width":745,"medium_large-height":466,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck.png","large-width":745,"large-height":466,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck.png","1536x1536-width":745,"1536x1536-height":466,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck.png","2048x2048-width":745,"2048x2048-height":466,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck-743x465.png","card_image-width":743,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/11\/Image-3-CompositeCheck.png","wide_image-width":745,"wide_image-height":466}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Other new checks in this release include those that assess different aspects of a feature\u2019s integrity. These include checks that assess Z-values to identify those which exceed a threshold when compared to other values on the same feature (Adjacent Vertex Elevation Change<\/a> check) or contain values outside a defined range (Evaluate Z Values<\/a> check).<\/p>\n Additional checks in this category are used to assess the quality of polygon features. One of them is the Polygon Sliver<\/a> check to find polygon features that are considered a sliver based on its shape and (optionally) area. This check is particularly useful when assessing the quality of features created from conversion of hard copy documents or generated when data of different scales or extraction requirements are combined.<\/p>\n In this video, see how Data Reviewer\u2019s configurable checks are used to implement data quality requirements.<\/p>\n"},{"acf_fc_layout":"youtube","start_time":"173","end_time":"590","youtube_video_url":"Finding errors in attribution<\/h2>\n
Finding errors in spatial relationships<\/h2>\n
Finding errors in a feature\u2019s integrity<\/h2>\n