00:01Welcome. My name is Jay Cary, and with me today is Michelle Johnson.

00:03We're members of the mapping and charting solutions team in Redlands, California.

00:08Today we've got kind of an introduction to ArcGIS Data Reviewer.

00:14By show of hands, how many folks have heard and/or are using Data Reviewer? Oh, good. Okay. Good.

00:22So what we're going to do today is we're going to start out by first talking a little bit about data quality…

00:28…what it is, how it's being defined, and also why it's important.

00:33We'll then get into a discussion of Data Reviewer.

00:36We'll start out by kind of identifying the major moving components of the extension…

00:40…we'll talk about different forms of validation that we do against our data to validate quality…

00:46…and then we'll talk a little bit later about how, once we've found all these errors, what do we do with them?

00:50How do we leverage them, how do we make our jobs easier when it comes to maintaining our data?

00:55And finally, at the end, we'll talk a little bit about resources available to you to help you learn a little bit more about Data Reviewer.

01:05So this first section, we want to talk about quality, what it is, define its different aspects as well as how do we…

01:11…you know, where you might find that technical guidance that tells you the quality and what is good quality.

01:15We'll then talk about Reviewer in a little more detail.

01:18At the end of each section, we'll be having a demo, and we'll take questions after the demo.

01:24If you do have a question that's just burning, you have to ask it right away, put your hand up; we'll try to get to you. Okay?

01:32So the first bit, before we even get into the technology piece is, you know, what is good quality, right?

01:39So when you look…like when you look into what is quality, there's lots of different aspects to data quality.

01:45How well are my features in the GIS reflective of features in the real world? Are my attributes complete and accurate?

01:52So all these kind of different elements to data quality conceptually tell us, describe its relationship to the real world…

01:59…a little bit about how it's structured in relation to other data layers…

02:03…an acknowledgment of where it came from, its source, as well as…

02:07…sort of an understanding that this data may have been manipulated through time and lineage that brings it to its current place.

02:17Given those elements, though, what is good quality data?

02:20So the question is different for every user; it may be different on a layer-by-layer basis.

02:25A lot of times, this is based on how are we using the data?

02:29So if I'm developing a transportation dataset that's going to be used for a national-scale map…

02:34…my accuracy requirements, my quality requirements are going to be much different…

02:37…than if I'm doing a large-scale project where I'm routing vehicles inside a city.

02:42So really, we need to think about how the data's going to be used.

02:46There might be some guidance if we're working for agencies of our contractors.

02:49It may be some technical specifications put out that kind of define what quality is and what's acceptable and what's not.

02:57Or client requirements may articulate that as well.

03:01Now this…we only talk about this in one slide, but I really want to make sure that everyone understands this is not easy to do.

03:08Defining quality, if you do it right, it's actually hard to do because it takes a lot of consensus…

03:13…a lot of understanding of, well, how's this data going to be used today and also what does the future hold for this data.

03:20'Cause there's a lot of, you know, impact to, you know…

03:22…gosh, I didn't realize we were going to use the data this way so we didn't build it that way.

03:26It's kind of a balance between spending too much money for quality data that you don't need necessarily…

03:31…but then later realizing, oh, I really did need that.

03:36So once these quality requirements are defined, we typically see them incorporated as a series of tasks and methods…

03:44…and things we do to meet that quality requirement.

03:47So quality control, just those things that you do in your production environment to make sure that you produce quality…

03:52…to a known standard as well as a methodology to assess it at some point.

03:57Now, a lot of folks have quality assurance that they do, which is the things you do in a production environment to ensure quality…

04:04…but in this mode, we're talking about quality control, which is those distinct things to check data.

04:10Check it before it goes out the door to a client, check it before we publish the data.

04:16Normally you'll see these kinds of requirements defined within a quality assurance plan.

04:20So in a lot of…if you're a contractor, you may have a quality assurance plan for project delivery…

04:25…that might also stipulate certain data requirements for you.

04:28If you're a government agency, you may have quality assurance plans that define…

04:32…on a layer-by-layer basis what your quality requirements are.

04:39So one of the things, and this sometimes is surprising to leadership in some cases…

04:44…but, you know, poor-quality data is expensive in a lot of different ways.

04:49It's expensive to fix after the fact.

04:51Having technicians go back and correct something that should've been found during production, that's time-consuming.

04:58It impacts users and decision making.

05:00If folks are using your data to produce cartographic products or do analysis…

05:05…all that becomes suspect if you've got bad-quality data or they weren't checking it.

05:09So really, regardless of whether you're a data producer or a consumer, you know, we all need to kind of work…

05:15…to have an understanding of some…you know, what is acceptable quality for the task I have at hand.

05:20If we're data producers, of course your reputation is at stake.

05:25Putting out bad data, not meeting your customer requirements, that could affect funding lines, reputation, things like that.

05:32So things to consider. You know, we all run spell checker on our Word docs before we shoot them out to have folks review them.

05:38The IT guys install patches and virus scanners to ensure a quality experience with desktops.

05:44You know, the same could be thought of with GIS data.

05:51So ArcGIS Data Reviewer is an extension to desktop ArcGIS…

05:55…and it provides a complete quality control environment for managing data quality through a life cycle.

06:01So we have, you know, rule-based workflows for the automated validation of data.

06:06We have interactive tools to help facilitate manual quality control review of data…

06:11…as well as a framework for managing those errors through what we call the sort of error life cycle.

06:19Because a lot of these processes are automated, it definitely saves a lot of time in terms of validating your data.

06:26In the long run, if you implement quality controls at really more as an ongoing part of how you do business…

06:32…in the long run, it may actually reduce the amount of work you do through time.

06:41So a little bit about the framework for how Data Reviewer manages its error life cycle.

06:47During the review phase, you'll take and use Data Reviewer's tools, automated checks, interactive tools…

06:54…to find and discover errors, anomalies in your database. And you'll discover those.

07:03As they're being found, they're written to a consolidated location and archived.

07:09During the correction phase, whether it's you or a contractor working on your behalf, they'll use the core ArcEditor tools…

07:15…to fix those errors, and Data Reviewer enables that person correcting the errors to annotate in the database…

07:22…Was it fixed; is it really an error? If it is an error, how did I fix it and who fixed it, and when it was fixed.

07:30This is especially helpful when you outsource data production to other shops where you might want to have a little dialog about…

07:36…well, I found these errors; you fix them, and back and forth.

07:41And finally, the verification stage.

07:42A lot of folks do this optionally, but it's a good idea to, once they've been corrected…

07:46…the person who found those errors has the ability to go back, verify the corrections were applied appropriately…

07:52…but also maybe rerun some of those validations…

07:54…and make sure that no new errors were introduced as a by-product of that correction going on.

08:03So there's a lot of moving parts in Data Reviewer.

08:06So essentially what, when we talk about writing errors and managing error life cycle, we do that through the Reviewer workspace.

08:12This is a geodatabase--could be file, personal, enterprise geodatabase…

08:16…a series of tables for storing spatial as well as attribute information about the errors we discovered in our database.

08:24We interact through this workspace through what we consider a Reviewer session.

08:29It's very similar to an edit session with ArcMap, and the purpose of an edit…

08:34…a QC session is that we're opening that workspace to interact with those records…

08:39…write new records, move errors through their life cycle.

08:44Once we've got to that part, we're ready to go.

08:47We have a workspace, we have a session, and now we can start exercising the various tools to discover errors in our database.

08:54This might mean running automated checks initially, might mean going through and doing it in a batch mode.

08:59We'll talk a lot about the automated data capabilities, automated data validation that Reviewer has as well as visual review.

09:08We'll talk quite a bit about visual review; very time-consuming process, but we try to help facilitate that with some tools.

09:17Once we've written those errors to the workspace, we use the Reviewer table…

09:21…to actually interact and visualize those error records, sort them, summarize them.

09:26Technicians use this tool to navigate to the errors and to update the status of that error as we correct them and verify those records.

09:39So once we've done our quality control review, and depending on your mode of how you're operating…

09:44…if you fix it or someone else fixes it, there's a whole host of reporting tools at 10 that have been introduced to help summarize…

09:52…and kind of make it more easily readable formatting of our errors in multiple different ways.

09:58So this might be a management report that's produced, and it's very quickly generated…

10:04…to give other folks an idea of the scope and extent of our errors.

10:09We'll do a demo of that, a couple different variations, show you how that new reporting capability is.

10:16Alright. So for our first demo, Michelle's going to be going through…

10:19…and we're going to do kind of a high-level pass over of Data Reviewer…

10:23…identify the major moving components and run a couple batch jobs.

10:26Later on we'll get into a little more detail about how that works, okay?

10:34Actually at this demo, I won't be running any batch jobs, but I will be running an automated check.

10:41So with Data Reviewer, what I wanted to mention is that it is a separate install to ArcGIS Desktop…

10:50…so if you go through, if you go and look at the toolbars, and you don't see Data Reviewer in here…

10:55…it means you do not have it installed on your computer.

10:59So it is a separate install; however, it is a standard extension to Desktop, ArcGIS Desktop.

11:06So let me add the Reviewer toolbar. Here we have some…

11:11…basically all the tools that you will need to perform your quality control on your data.

11:15So we have some tools here that is used not every single time, but it is used quite often, so it's down in this menu.

11:24We have our Session Manager button here, and I'll be showing that in just a moment…

11:29…and then we have some tools to manually inspect the data…

11:33…and then here we have a suite of checks, automated checks, that you can run against your data.

11:38Then at the end of the toolbar here, these are helpful for performing visual review of your data.

11:46So let me show you an example of an automated check.

11:49Here I have a database with address points, building footprints, and some parcels…

11:56…and what I want to do is a spatial type of check where I'm validating my address points against my building footprints.

12:02So I have a rule that my address points must fall within the building footprint…

12:07…so I want to find all the points against my building footprints that are not within the footprint.

12:14So I can set my spatial relation to within, I can use the NOT option, then I can run this check on my current extent.

12:28And it found two records.

12:31Because this is a point feature, it will pan to that location, so let me just change my scale.

12:40And here I have a point that is next to my building but not quite within the building footprint.

12:46And, again, if I navigate to the next error, similar type of situation. So these are errors.

12:54Now, we looked at these errors, but we want to be able to store these errors in the Reviewer table…

13:00…so in order to do that, you have to start a session.

13:04So here is the Session Manager dialog, you browse to the workspace you want to store your errors in…

13:10In this case, I have browsed to a file geodatabase. …and then you can select your session.

13:18I'm going to go ahead and select this one; I already have some records in this session.

13:25To access the records, you open up the table, and here stores all the errors that you have found through your QC…

13:33…whether it's automated or visual.

13:36And by double-clicking on a record in the Reviewer table, it will navigate you to that feature.

13:46And this is really helpful when you're going in through the correction process and having to make all the fixes to this data.

13:55So the table has information on what feature class, you know, has the errors, the type of error that it was, and the error status.

14:05So if we group the column, we can see that these are all the different types of errors that we have.

14:13So we have some invalid domain values and we have some points that are not within a poly…

14:18…some polys not within our polys.

14:20So you can get a quick summary of the Reviewer table by grouping the columns.

14:26So that's just a quick overview of Data Reviewer.

14:30You get the toolbar, you've got the table, you have the automated checks, and then there's some tools of visual QC…

14:36…which I'll be showing in the next demo, or the next couple of demos.

14:42Any questions at this point? Yes, sir.

14:46[Audience question] How do you get the…how does it pick up the reviewer technician?

14:50Is that something that when you log in it recognizes who you are?

14:55Yes. So this is based on your Windows login, and then you can modify this too.

14:59You can just go ahead and edit it to whatever name you would like if you wanted to. Yes, sir.

15:07[Audience question] The check you did there was real similar or looked similar to a regular topology check.

15:13What's the difference, or is it the same or different or better or…

15:18So the question was that this check that I performed, the geometry on geometry check, is very similar to database topology.

15:25And yes, it is. However, it's not the exact same. It is different.

15:30Database topology resides in your geodatabase. There's some rules that you set up.

15:35There's more rules, I think, that you can use with database topology than what geometry on geometry may offer.

15:41However, the advantage is, of geometry on geometry, is that your features don't have to reside in the same feature dataset.

15:48They could reside in different geodatabases.

15:51Also you can define your rules to the attribute level, where you can apply a SQL WHERE clause.

15:55And I believe database topology, you can only go down to the subtype level, so…

16:01Okay, so we'll go ahead and go back to Jay.

16:07Alright, thanks, Michelle. So now we'll dig a little bit deeper into this automated data validation.

16:16So we'll talk a little bit more about some of the checks we have out of the box…

16:20…and talk about how we can take those and run them en masse.

16:25So this is really the strong suite of Data Reviewer, its ability to take the…

16:30So we talked about the quality assurance plans that these business rules are defined within there.

16:35If you think about it, those business rules are sort of kind of a logical sort of discussion about quality…

16:40…but we can take those business rules and implement them in an automated way.

16:45The value of being able to automate it is, number one, it can be done, requires really little human interaction…

16:51…once those rules have been built.

16:53Also, it, because it's automated, you can oftentimes validate 100 percent of your database.

16:59So it's very efficient from that point of view.

17:02It's also this idea that we can persist these through time, so just like Michelle configured this check…

17:08…you can also configure these checks and then distribute them throughout an organization.

17:12We'll talk a little bit about how we do the detail of that later.

17:18So this is a picture of the Data Reviewer check poster, which can be downloaded from our product page on

17:24And what it does is it outlines all the different checks that we have with Data Reviewer.

17:29At release 10, we have around 42 different checks available, and they're organized into these 11 functional areas.

17:36You might have noticed that when Michelle was pulling down that Check dialog…

17:39…you saw sort of that functional breakdown of checks.

17:45Some of the real popular check categories are things like table checks…

17:48…where we have different methods for evaluating the attribution of our database.

17:53One of the real powerful checks there, though, used quite a bit, is the execute SQL check.

17:59So an example of the execute SQL check from the water utilities domain is that there's a simple business rule.

18:06"Water mains installed after January 1, 2000, should have a material attribute of either iron, ductile iron, or PVC."

18:15Using the execute SQL check, we can build a SQL query that essentially validates our features…

18:22…based on a SQL WHERE clause, and then when it tests the data, it just pulls up records that don't meet that requirement.

18:29Now, a lot of this can be done through the geodatabase; we talked about topologies.

18:35The value here is that we're actually validating multiple attributes, not a single value like you would with a range or a list domain.

18:42So we can build a query that says if it's this, then it should be that…

18:45…so you can look at attribute combinations and make sure that they're valid.

18:51Another category of checks is our feature on feature checks. We saw a demo of the geometry on geometry just now.

18:57This is another ability to validate sort of the spatial relationships between our features…

19:02…and, in some cases, have a much more granular way of doing that.

19:07So again, the geometry on geometry check, very popular, very powerful 'cause it has lots of different capabilities.

19:13So another example from the water utilities industry is the idea that hydrants need to be connected to a hydrant lateral.

19:20So that business rule can be implemented using the geometry on geometry check.

19:23So we have hydrants as feature class 1, hydrant laterals as feature class 2…

19:29…but in this case, like Michelle showed, we have the ability to select a NOT condition.

19:34So here we're looking for ones that don't intersect one another.

19:38So the results of that kind of check would be hydrants that are completely disconnected from the network…

19:42…as well as hydrants that are connected but connected through the incorrect subtype.

19:46In this case, it's hooked up through a service lateral. Okay?

19:51And as Michelle talked to you a little earlier, you can apply also SQL WHERE clauses…

19:55…so if you really need to get down to specific features…

19:58…you can build a definition query basically to filter out things that you don't want to have queried.

20:05So the advanced check category, lots of new checks were introduced at 10.

20:09These are advanced checks that are…don't really fit conveniently in any of the other functional areas of the check poster.

20:18One of those is really an important one for the utilities industry is the valency check.

20:25In this example, we're looking for reducers that have to connect…

20:28Reducers are required, so you've got two pipes coming together, and they're different sizes.

20:33We need to have a reducer to take it from size X to size Y.

20:36So using the valency check, we can check the relationship between lines and point features…

20:41…and when they don't meet that requirement, we want to flag it as an error.

20:44So in this case it's a little bit different than all of our other checks, 'cause it's inferring and finding missing data.

20:50Normally, automated checks only run against, you know, features that exist in our database.

20:54But in this case, it's looking at a relationship between two existing features and inferring some missing data.

20:59In this case, we have an 8-inch diameter pipe coming to a 12-inch diameter pipe; there's no reducer in there.

21:06So it's finding actually missing data for us, so it's kind of an interesting check from that perspective.

21:13Other new checks introduced at 10. The metadata check.

21:16If your organization authors metadata, this is [unintelligible]-level metadata, you can validate in an automated way…

21:22…your metadata against the standard, a metadata standard…

21:25…or against specific content standards that you might have for your organization.

21:29We talked about the valency check.

21:31The custom check that enables you to extend the Data Reviewer framework by developing custom code.

21:38So if out of those 42 checks, none of them meet your requirements, you can get a developer involved…

21:43…to build you a custom check that'll still operate within the framework of Reviewer.

21:47The results are written to the Reviewer workspace as would any other check result…

21:51…so you can have a similar place for your errors to go.

21:55Same, same with the topology rules check.

21:57If you're using database topologies, instead of dealing with those errors in a separate way, you can import those errors…

22:03…into the Reviewer workspace and have a common place to work and manage those errors…

22:09…without having to go to two different places. Question?

22:12[Audience question] How is the metadata check different from the USGS MP Translator tool for all the [inaudible] in ArcGIS?

22:19So the question was, How does the metadata check different between the Metadata Parser, right?

22:25So one of the value-- So that works great with FGDC metadata, but a lot of folks are moving to ISO metadata.

22:32So the metadata check supports both schema validation, which is, I think, what the Metadata Parser does, correct?

22:39So you can validate based on the schema, but it could also be FGDC schema or ISO 19115 standard metadata…

22:46…or the North American Profile or INSPIRE.

22:49We go a little bit beyond schema validation with the metadata tool because you can build specific element value rules.

22:57Point of contact must be John Doe; phone number must be properly formatted US phone number.

23:03So it goes beyond--for metadata, it's quite extensive in terms of validating your metadata…

23:09…and like any Reviewer check, it just whips through your whole database if you need it to.

23:13Does that answer your question? Okay. Sure.

23:19So we have these automated checks that we can configure and run.

23:22Michelle showed you running it sort of in what I call an ad hoc mode; just interactively configure it and run it.

23:29Now that works fine, but what you'll end up finding is you'll want to configure it once and run it over and over and over.

23:35So we had this idea that you might want to configure these checks…

23:38…and the input from these checks might come from a lot of different sources.

23:41You might get business rules from…just from your own experience.

23:44You might have technical requirements coming down from your customers.

23:47You might have a quality assurance plan.

23:49So the idea is you take all those business rules that are maybe through disparate places, and you implement them in Data Reviewer… configure…you identify one or more checks that answer that business rule; you configure them…

24:01…but then you persist them on disk and you save them.

24:05They're saved down to disk as a small XML file.

24:08If you're a one-person shop, you just can rerun that over and over and over.

24:12And we'll talk about ways of running it in a few minutes.

24:16What you see here in the bottom right corner is sort of that idea that, you know…

24:19…we've got all these business rules that we've implemented with Reviewer checks.

24:22We store them all in one place, and we can share them amongst users.

24:26So not everybody needs to be a QC expert; they just need to know how to run a batch job.

24:34So batch validation, that's a tool that we'll see in a few minutes that takes these configured groupings of checks…

24:41…that we've defined for our data and enables us to run them all at once.

24:47And we'll get into some detail about the extent and how those different things work…

24:50…but the idea is to create it once, run it multiple times.

24:58So you can always run these interactively through ArcMap, which is a way a lot of folks do it…

25:02…but it does tie up your computer while it's kind of grinding through all these checks.

25:06So we've introduced a whole bunch of different ways of running these automated validations.

25:11The Reviewer service, that's a Windows service.

25:13You can schedule to have these automated validations happen maybe during off hours.

25:18Let's say we check--everyone goes home at five o'clock; let's set up a schedule for it to run at night while we're not there.

25:23First thing in the morning, we come back in, there are our error results.

25:28Also from a command line, if you're into running a command line, or as a part of a Python script.

25:33A really neat thing at 10, though, was if you're using Workflow Manager to manage your production workflows…

25:40…within your organization, a number of custom step types have been provided at 10 so that QC, automated QC…

25:48…could just be a step in a workflow, and it just runs; you don't even have to run it from ArcMap.

25:55Alright. So we'll go ahead and do a quick demo and talk a little bit more about automated data review. Michelle?

26:00Thank you, Jay. Alright, so I showed you how to run a check by itself through the toolbar…

26:11…but what we really want to do is create what we call a batch job to store all of our checks in.

26:15So through the Batch Job Manager, this is where you can create your batch job.

26:21First, you need to create a group, and then within that group you can add a check.

26:27So I can create a domain check, and the reason why I would want to create a domain check…

26:31…is this data's recently been migrated from shapefile into the geodatabase…

26:35…so I want to ensure that all of my features are adhering to the domain constraints that we have defined in our geodatabase.

26:42So let me select my feature class, Address Point…

26:48…and I want to create a domain check for all of my feature classes in my table of contents.

26:55So let me duplicate that check…

26:58…and I can select all my feature classes, and then it'll create the checks for those feature classes.

27:04Now I can also move around my checks from group to group…

27:10…and I can also rename my group to something a little bit more meaningful.

27:16Now having your groups is a great way of organizing your checks.

27:21So I'm going to go ahead and add the check that--another geometry on geometry check…

27:28…where I'm looking for my building footprints that are not within my parcels.

27:36Again, set my spatial relation; use the NOT option, and let me rename this group.

27:48So now that I have my checks configured, I can save this out into a batch job file.

28:03Let me just navigate to my folder.

28:15Now that I have this batch job file, I actually have some other checks that I have configured in another batch job file…

28:21…that I want to bring in to this one.

28:23So I can insert that by going to the Insert button, selecting my other batch job…

28:28…and now I have all the checks from the batch job I just created and this other batch job I inserted.

28:37So I kind of wanted to highlight some of the checks that I configured, so here are attribute checks.

28:43Now, these are basic execute SQL checks.

28:45Notice that I was able to give it a title, select a feature class, and pass a simple WHERE clause.

28:54I also have this duplicate geometry check.

28:59Here I'm looking for duplicate address points. I'm comparing features within the same feature class.

29:04I have the ability to compare attributes, so if they have the exact same attributes then I know it's an exact duplicate.

29:11And I also have the option to ignore feature-level metadata attributes, like the last editor and last update fields.

29:21And then also I have, in my first demo, shown you how to configure the geometry on geometry check…

29:30…looking for the address points not within the building footprint.

29:32Now what I didn't tell you is that my Address Points actually has a field, Address Type, that identifies that point…

29:40…whether it should be within a building or within a parcel.

29:43So this is where I can define at the attribute level what I want to validate.

29:47So here I have my address points where the address type is building, and I'm comparing it to building footprint.

29:53And then this one here, I have my address point where my address type is parcel and comparing it to my Parcel feature class.

30:05So now that I've combined both batch jobs, I can save that out to a new batch job.

30:14And now that I have my batch job, I want to be able to run this batch job on my data…

30:17…and I want to be able to store these errors in a session.

30:20So let me go ahead and start a new session; I'm going to use my Batch Validate session.

30:26Sessions are a great way of organizing your errors.

30:29Now, what I like to do is have a session for automated QC then have a separate session for visual QC.

30:36And if I have multiple people performing visual QC, then I'll have a session for each one of those technicians doing the visual QC.

30:45To run that batch job, I'll go to Batch Validate.

30:49I'll add my batch job; it will load up all the checks. I'll validate my batch job.

31:01What validate does is to ensure that all of the feature classes that I'm validating are available to me in the table of contents.

31:08And I have the option to validate on a selection set of feature; the current extent; a definition query…

31:13…[unintelligible] definition query in ArcMap table of contents; the full database.

31:17And if I'm in SDE, I have the option to validate on the features that have been edited.

31:22It'll compare the current version to the parent version and identify those edited features.

31:28So I'm going to go ahead and run this on the current extent, and it's running all of those checks.

31:33And when you run it in a batch job, it'll automatically write those errors to the Reviewer table.

31:39So it has found 273 errors within this extent.

31:46I open up the Reviewer table, and you can see that I have a table full of records.

31:52So I can group by that check title…

31:55…and I can see that I have majority of my errors are with my domains and within the Road Centerline feature class.

32:06So that's a way that you would probably want to run your automated checks is through batch.

32:13So through Batch Job Manager, you can create your batch job…

32:17…and then through Batch Validate over here, you can run your batch job.

32:21So now, do we have any questions on automated QC? Yes, sir.

32:30[Audience question] Can you set it up so that it automatically…that does a batch on a certain extent...

32:40…or do you have to manually [inaudible] out locations for…if somebody's going to do editing?

32:47I mean, can you just bring in…

32:50Yeah. So the question is, Can you set up Reviewer to automatically validate an extent?

32:55Now I think maybe working with Workflow Manager, with Workflow Manager you have your area of interest.

33:01 You can run the… We integrate very well with Workflow Manager…

33:06…so we have custom steps that you can configure in Workflow Manager to execute your batch job.

33:12So you can, you know, using Workflow Manager, go to the area of interest and then…

33:17…run that batch job on that area of interest. Yes, ma'am?

33:22[Audience question] Do you run the same batch job on multiple feature classes at once, or would you…

33:27Can you run the batch job in batch, or do you have to do it on separate, each one separately?

33:34So the question is, Can you run the batch job in batch? I'm a little…

33:40[Audience question] If you have 14 separate feature classes that all need to be validated at the same time…

33:44…like, you know, maybe midnight one day, can you run it all at once, or do you have to do it…

33:49So what you would do is configure a batch job with all of the checks for all those feature classes…

33:54…and then you run that batch job once. You can create multiple batch jobs.

33:58Maybe you want a batch job for each feature class; it's a different way of organizing your checks.

34:03And you can run, if you use the Reviewer service, you can select all of those batch jobs…

34:08…and, you know, schedule it to run at midnight. Yes, sir.

34:16[Audience question] I'll talk on that screen where you can check Current Extent or Full Extent.

34:24Do you have the choice to like do it within a polygon feature class?

34:32So if…so the question is, Do you have the option to validate features within a specific polygon? And…

34:41[Audience question] Just like we, you know, we're water, just like these examples you've been showing.

34:47And like we put a project in, we put what we call a progress polygon around it…

34:53…and so you can identify everything related to that project.

34:56So you could then, after you've drawn that polygon, do your data check within that as opposed to the whole extent because…

35:05Yeah. [Audience question, cont.] …within your extent, there could be features that are ranked, you know, 1950…

35:11…that aren't going to have those attributes, but you don't know.

35:13So the question is, you know, if you have a specific extent that you only want to validate and not the features outside of that…

35:21Well, not extent, but the polygon.

35:23…with ArcGIS Desktop, running Reviewer in ArcGIS Desktop, you do not have that ability.

35:32What you would have to do is select your features within that polygon first and then validate on your selection set.

35:39I think with Workflow Manager, you can pass that area of interest, and it'll just validate those features within that polygon.

35:46And then at 10.1, we have--you know, we currently do have a GP tool where you can run your batch job…

35:54…but at 10.1, we'll be able to pass that polygon and just validate those features within that polygon.

36:04Alright, Jay? Oh.

36:05[Audience question] Can't you do it against a version as well?

36:08Yes. You…whatever data is-- So the question is, Can you run your checks against a version? Yes.

36:16So if you're in an SDE database and you have a version, whatever version is loaded in your ArcMap table of contents…

36:22…it'll validate those features. Alright. Thank you. Jay?

36:29Alrighty. So we've talked about automated data review…

36:35…and you've seen how quickly we can kind of rip through a database in finding errors…

36:39…a lot more errors than your technicians will be able to fix probably in one day.

36:42But let's talk a little about, you know, the things we can't do with automated checks.

36:47You know, even though we have 42 different checks, there's still going to be some things that have to be done visually.

36:51So we'll talk about how we can help facilitate that process, and we'll do a demo showing some of those tools as well.

36:58So really, you know, visual data review, a lot of folks probably have traditionally used that for their entire QC process.

37:05And, you know, in general, you know, it's a decision-making process; it needs to have folks who are understanding of the data…

37:11…can make consistent judgment calls today, tomorrow, the next week…

37:14…however long this process is for visually interrogating our data.

37:20It is a time-consuming process, though, right? Takes a lot of time to do.

37:25So we'll talk about some ways a little bit later about how we can kind of help facilitate a little bit there.

37:31Data Reviewer does help, though, kind of organize and provide a structure…

37:34…for how you might go about doing visual QC of your data.

37:38Has some tools for helping managing that process, as well, along.

37:42And again, because you're using a single workspace to log all your errors…

37:47…errors you would get through visual QC are then stored along in a single repository so that when you--

37:53…you have a complete record of the errors from your database.

37:58So one of the tools we have to help kind of manage this process is our overview window.

38:03This is just a simple window in ArcMap that allows you to visually look and see the extent of your study area…

38:10…divided into smaller pieces using a grid, and then some simple navigation tools to move a technician who's doing visual QC…

38:18…from one cell to another cell to another so they don't miss anything as they're doing their QC review.

38:25We also have the ability to toggle these cells as having been reviewed or not reviewed.

38:29So you can…you know, obviously you may not finish visual review in a single day…

38:34…so the next morning you come back in, you'll know where to pick up again.

38:39Now because it is so labor-intensive to do visual QC, a lot of organizations don't do 100 percent review of their data.

38:47So at 10 we introduced a new check, the random sampling check, which allows you to do a random sample on a…

38:53…using a number of different methods to arrive at a random sample of features, the idea being…

38:58…I'm going to take a sample; some organizations may say, hey, I need a, you know…

39:02…I have a, you know, take 500 features. Other ones might be a percentage of the features.

39:08We'll also have the ability to autocalculate that random sample size based on a confidence level and a margin of error.

39:14So if you don't know how many features you need to, what would be a statistically valid sample…

39:19…you can use the autocalculate to figure it out for you based on your confidence.

39:25The random sample results are written to the Reviewer workspace like an error.

39:29That way you can use the Reviewer table to navigate to those randomly selected features…

39:34…visually review them, and mark them pass/fail.

39:39Now later on, we'll show you some of the reports that can leverage that information…

39:42…to help you have a concise report of whether the data's passed or not from your visual review.

39:47And finally, also, by polygon grid. So a lot of organizations don't do a random sample of features.

39:52Maybe they have a grid that they do a random sample of the grid and then 100 percent visual QC of the features within that grid.

39:59So we also support that type of sampling as well.

40:04In an enterprise environment, folks may want to be able to compare maybe different edit versions to one another.

40:10At 10, we introduced a Version Differences tool…

40:14…similar to the core tool where you can check the differences between parent and child versions.

40:20This one actually allows you to compare sibling versions.

40:23So if I have multiple editors working, and as the QC manager, I want to see what they're working on…

40:27…to make sure they don't step on each other's toes, I can use the Version Differences tool…

40:31…to compare those two sibling versions before they post and reconcile.

40:36The results are written to the Reviewer workspace so I can go back and review them as I would any other error.

40:44Other ways of writing errors to the Reviewer workspace include the Commit To Reviewer Table tool.

40:50So essentially, any process that produces a selected set of records--maybe it's a network trace, maybe it's a model.

40:57Whatever that selected set of records are, you can commit those as errors to the Reviewer workspace.

41:03Also interactively you can commit them, as well.

41:09The Capture Missing Features tool is for those times you know there's just data missing…

41:13…and I really quickly need to note it, provide a little bit of descriptive information about what's missing…

41:19…and how severe is it of an issue so that my technicians can go back later and add that feature.

41:25If I'm particularly energetic, I have some sketching tools that I can sketch point, line, poly geometries.

41:32And if I'm a good enough sketcher, maybe the technician just takes that, copies it into the database, and they're down the road.

41:41Alright, so let's go ahead and we'll take a quick demo of the visual QC process. Michelle?

41:48Thanks, Jay. So as Jay mentioned, automated QC is very useful, but it cannot catch everything.

41:57So I've been assigned to review map sheet 2217…

42:02…and my boss has asked me to check my building footprints against the image data that I have.

42:09So what I'm going to do is create a grid over my map sheet area, and this will help me perform a systematic visual QC of my data.

42:19So I've just opened up my Create Polygon Grid wizard…

42:24…and so what I need to do is browse to the location where I want to store my grid, give my grid a feature…

42:31...give my grid a name, then I'm just going to use this first option to drag a bounding box over the area that I want to create my grid.

42:44So I'm just creating that box over my map sheet 2217.

42:49And I want to create a 4 x 4 grid, and then I would click Finish.

42:53Now, I've already created a grid for this demo, so I'll just go ahead and cancel this and turn on the grid that I've created.

43:01This is a 4 x 4 grid and then I can use this grid in my overview window that Jay mentioned.

43:08And if I double-click, if I use the Select tool and double-click on my grid cell, it'll navigate me to that location.

43:16Now if you notice over here, this grid cell's already shaded, so I've already reviewed that area…

43:21…so I know that I need to move on to the next cell.

43:25So I'm reviewing this area, and I can already see that I've a missing building footprint.

43:31So then I can use the Flag Missing Feature button to identify this missing footprint.

43:36I select my feature class and then enter a status of Add, and then that record will be written to the Reviewer table.

43:45Once I've finished reviewing this area, then I can mark this cell as being reviewed by just clicking on the Change Cell Status.

43:55And then I can use the navigation tools to move on to the next cell.

44:01And in this area, I can see that I have some building footprints that are not aligned to the buildings in my image.

44:08Now, the automated QC would not have caught this, right?

44:11So the checks that I configured in my automated QC is looking for my points within my building footprints; that's okay.

44:19And I also had a check looking for my building footprints within my parcels, and that's okay.

44:25So the automated check would not have caught this, so this is why visual QC is important.

44:31So I can select those features that are misaligned and commit them to the Reviewer table, saying that they need to be moved.

44:55So now that the rest of…I've looked at the rest of this data, and it looks okay, so I can mark that as being reviewed.

45:01So you kind of get the idea of how the systematic review works with Data Reviewer.

45:07So now that I've done my visual QC, I wanted to show you the records that were written to the Reviewer table.

45:17So if I double-click on the record, it will navigate me to that location, and this first one here is a missing feature.

45:23So here's my missing feature, but you don't see any indication of that missing feature…

45:27…so what you can do is click on the Symbolize Reviewer Feature Records button…

45:33…and it'll symbolize all the records in the Reviewer table in your map display.

45:37So there's my missing feature.

45:42So as Jay had mentioned, performing a visual QC is very time-consuming…

45:47…and if you have lots of data and maybe not enough resources…

45:51…what you may want to do is perform a sample of your data and then perform the visual QC on that sample.

45:59So I would like to show you that sample, sampling check that we have.

46:04So let me end this session and start a new session for sampling, and I'm going to configure my sampling check.

46:17And I want to sample my building footprints.

46:19I'm going to use this autocalculate method and then use the default parameters for the confidence level margin of error.

46:29Then I'm going to run this on my entire database…

46:33…and it'll randomly select the features within this building footprint feature class.

46:40And then it'll write all the records, if I so choose, to the Reviewer table.

46:47So there's over a thousand records written to the Reviewer table.

46:51Now if I open that Reviewer table, I'll have all of my records in here.

46:58I want to point out this review status. The review status says, "Needs review."

47:02So once you've done your sampling, all the records in there will have a status of Needs review.

47:08And then you would double-click on that feature, inspect it, and then, if it looks okay, then you can update the sampling status.

47:17So if it's acceptable, you can pass it, and then the information in the Verification column will get updated…

47:28…and also the review status will be updated.

47:31So then you can move on to the next one, and if that's acceptable, you can update that status.

47:41So you get the idea of how the sampling works.

47:44So if this one fails, it fails, but note that the Verification column did not get populated.

47:51So this way, when the person goes in and makes a correction to these sampled features…

47:55…they can populate the correction information and then the verification information separately.

48:03So what this sampling method that we used, autocalculate, it tells…

48:08…based on the number of features you have in your feature class, it determines your sample size.

48:12And also, this acceptable failure threshold identifies the number of features that you're allowed to have…

48:22…before this dataset's considered failed.

48:25And we have a report that we can generate, and I'll be showing that in the next demo, so it'll tell you whether it passes or fails.

48:35So those are two ways that you can use Reviewer for performing visual review of your data…

48:40…using the overview window to systematically visually QC your data and then also using the sampling check…

48:46…to get a statistical sample of your data and review those features individually that way through the Reviewer table.

48:56Any questions on the visual QC?

49:05Okay. Alright. So now we've been talk--

49:10We first started talking about automated data review, logging lots of errors to our Reviewer workspace.

49:15We've logged a bunch of errors to our Reviewer workspace based on our visual QC review.

49:21So let's go into a little more detail about how we work with those records, and more importantly…

49:25…how do we summarize and report those findings out, you know, to other folks, maybe contractors or to management.

49:33So as you've seen in the demos, the Reviewer table's really an interactive table.

49:37It allows you to…there's a relationship between those errors and features in your map…

49:43…so double-clicking on them navigates you to the error.

49:45The value there is that as technicians work through the data, they can very quickly get to the problem that's at hand.

49:53We also talked about the verification statuses, the ability to--sorry.

49:59…to keep the life cycle of that error through its--sorry. …to keep track of its error through its life cycle.

50:06So we've changed, you know, as it's being corrected, we'll note that in the database and then we'll have the verification as well.

50:13And it's all being tracked in a table.

50:15You might have noticed we were also grabbing the user name and a time-date stamp.

50:18So we can kind of keep track of our errors actually being fixed in the database or not.

50:24You know, we can very quickly look at the Reviewer workspace to see we've got a bunch of errors; where are they at in the process?

50:30Are they getting fixed or not?

50:37We've already seen an example of some of the summarizing work we can do with Reviewer, with the Reviewer table…

50:43…so simply dragging and dropping, dragging a column to the gray title bar there allows us to group our errors.

50:49And it's really helpful to kind of get a first-blush look at the trends in our data.

50:52Are we seeing lots of errors in certain feature classes?

50:56And by further subgroupings, we can kind of look at it and think, well, are these systematic errors in my data.

51:03We had an earlier example of that domain check problem. That might be a real simple fix.

51:08Even though we may have hundreds of records in error, that might be something we can fix quickly.

51:13Whereas when we don't see systematic errors, that might mean for a manager to sort of think about…

51:17…well, this might take a little while to fix.

51:23One of the really neat things we brought out at 10 are a whole host of new reports that are generated for you…

51:28…out of the Reviewer workspace.

51:30We'll demo this tool, but essentially what it does is allows you to create an automated report in Excel…

51:36…and break it down by different categories, by feature class or origin table, by subtype.

51:41We have the sampling report as well.

51:43And the value here is that it's literally, click, click; there's your report.

51:46It's in Excel so you can then go and customize it in some way if you want to do further summarization or filtering of those results.

51:54It's a presentable report, though, right out of the box.

51:59Alright, so in this demo, we're going to work with the Reviewer workspace; work with the table a little bit…

52:04…work some errors through their life cycles, as well as run a couple reports to show you how that works. Okay? Michelle?

52:13Thank you very much, Jay.

52:15Alright, so here I have my Reviewer table open, and this is where we store all of our errors…

52:20…and it's kind of the heart of Data Reviewer.

52:23This is where you keep track of your errors; the information about your errors…

52:26…so the title, the feature class, the check that was performed…

52:31…and if you use severity, you can log the severity of the error, whether it's a critical error and needs to be fixed immediately…

52:40…or you know, if it's maybe not so critical.

52:43It keeps track of who found the errors and also keeps track of the correction status and the verification status…

52:51…so that's the error life cycle that Jay was talking about; who made the corrections and then who verified those corrections.

52:59So with the Reviewer table, you can double-click on a record; it will navigate you to that feature…

53:04…and then you can go in and make your correction to that feature using the core tools.

53:10Now, once you've made your correction, then you can keep track of that correction…

53:17…by right-clicking on the record and enter your correction status.

53:22So I've resolved this, and then it will update the correction status information.

53:28So I briefly showed you how you can group records in the Reviewer table.

53:34One thing that I like to do is group my records by feature class and then I can sort them by the object ID.

53:42And this helps me focus my correction on each feature, if that makes any sense.

53:48So say, if you notice here, my object ID here is 467 and 467, so this one feature has two errors…

53:56…so I can fix both errors at the same time.

53:59And if I fix both errors at the same time, I can select both records and then enter in the correction status at the same time.

54:10So by grouping the record, it helps you focus your correction process.

54:14You may want to focus based on feature class, which I have done here, or maybe you want to focus it on the type of checks.

54:20Maybe you want to fix all your domain errors first or your, you know, attribute errors.

54:26If you group multiple feature classes, or multiple records, so say by feature class and the check…

54:40…you can get a summary of the errors that you have and then you can export this into a simple text file by going Generate Statistics.

54:58And it'll create a text file of basically that summary that you have in your Reviewer table.

55:05And then, if you want to, you can e-mail this to your manager so that he can get an idea of what those errors are.

55:11But if you want a little bit more detailed report, we do have that ability, new at 10, to generate reports.

55:19So here we can generate a report by--here I would like to generate a report by feature class, which is origin table.

55:32And it'll create this report in Microsoft Excel.

55:38And here I have my feature class, Address Point; my checks that I ran against that feature class; and the title.

55:46And then it keeps track of the features that I validated, the number of errors that were found…

55:50…and then it'll give me a percent accuracy of that check. And I can get the percent accuracy at the feature class level too.

55:59Here it's 99 percent, but if you notice that my domain check was at 97 percent.

56:07So this is a great way of identifying kind of the accuracy or the quality of your data…

56:15…is by running these checks and then creating these reports.

56:19Now I did that sampling in my previous demo, and I wanted to show you how you can create a report out of that sample…

56:28…that identifies whether or not your data passes or fails.

56:31So my sampling report is in session 5, so I can select which session that I want to create my report on.

56:40I'm going to select the sampling report, and because there are four different methods of doing your sampling…

56:51…it creates a worksheet for each one of those.

56:54So I did the autocalculate method, and here I have my building footprint; it tells me the total number of features I have…

57:01…the sampling number, and then the number of errors I'm allowed to have for this dataset to be considered acceptable.

57:10And now it tells me, based on the review that I did, I had 37 errors, so based on that, this passes.

57:20So this tells me whether or not, as long as my QC has been complete…

57:24…tells me whether or not this data is acceptable or not acceptable.

57:27It kind of gives you that pass/fail information.

57:36So with Data Reviewer, you're able to perform your automated checks and then use tools to perform a visual QC.

57:44All of your errors are stored in the Reviewer table, and then you can generate a report that gives you like the quality of your data.

57:54Do we have any questions at this time? Yes, sir, in the back.

57:59[Audience question] If somebody finds errors in like a statewide database…

58:05…can they send that error to somebody that's working that one [inaudible], working that same data…

58:10…and [inaudible]...they zoom to the errors then?

58:15So the question is if you have a large dataset and maybe somebody else is managing that dataset, and you find an error in it…

58:24…and you want to be able to send that information to the user and then they can look, zoom in to that location to see the error.

58:31You can export the features in the Reviewer table. If they don't have Data Reviewer…

58:37You'll have to export the features in the Reviewer table.

58:40If they have Data Reviewer, you can just export those records to another Reviewer workspace…

58:45…and send them that Reviewer workspace, and then they can just import those records and then zoom to it.

58:50But if they don't have Data Reviewer, by symbolizing the records here, you add these feature classes…

58:59…and then you can just export these feature classes using just the core functionality, Data Export.

59:05And then you can share that feature class or shapefile, whatever you export it to, to that user.

59:16Also, as Jay was reminding me, we do have the ability to--let me ungroup my table.

59:22…ability to export the entire Reviewer table into an Excel spreadsheet.

59:30So it will take all the records in here and just copy the contents into the Excel spreadsheet…

59:34…so then you can share that spreadsheet to the user as well.


59:42[Audience question] Can we set up the Reviewer workspace in SDE so that other users can also see the edits?

59:49Yeah. The question is, Can we set up the Reviewer workspace in SDE? Yes, you can.

59:53And actually, we have two white papers that talk about the best practices of setting up your Reviewer workspace…

59:59…in a SQL Server SDE and then Oracle SDE. So those can be accessed from our website.

1:00:07[Audience question] And then can you also set up like the sessions you have to specific group of users to access specific sessions…

1:00:13…and [inaudible] specific groups?

1:00:18The question is, Can you set up your sessions for specific users?

1:00:22[Audience question] Meaning if you have the QC and then the QA and [inaudible] sessions.

1:00:27Specific group of users can access only those sessions--

1:00:30Oh, setting permissions to certain sessions for the users. I don't…

1:00:35At this time, we do not have that ability to have only specific users access certain sessions.

1:00:44You may be able to do that through the RDBMS, but that would get pretty messy.

1:00:48'Cause you're talking role-level security at that point. Yeah.

1:00:52[Audience question] But can he have like roles who can do a specific [unintelligible]?

1:00:57If you have like a multiuser, could [unintelligible] QC [unintelligible], for example, [unintelligible].

1:01:08So you can set privileges in the Reviewer table to people who can only write to the Reviewer table…

1:01:16…or who can only read to the Reviewer table, so you can do it at that level.

1:01:24Question over here?

1:01:25[Audience question] [Unintelligible] feature class name [unintelligible] before taking…before running a session?

1:01:40Could you repeat that question?

1:01:41[Audience question] If you have [unintelligible] client, finds that are the same construction every time.

1:01:52So you have to run it, but every time with another finding. So feature class name.

1:01:59So, yeah. So the question is, You have data coming in, but the feature class name is different…

1:02:06…but you want to run the same checks.

1:02:08So with Data Reviewer, when you build your batch job, it is important to have the same schema…

1:02:13…so your geodatabase name could be different, but your feature class name will have to be the same…

1:02:22…for it to automatically re-source. I believe at 10 we do have the ability to, through Batch Job Manager and batch validate…

1:02:33…you can change the workspace of the checks, but you can also change the feature class.

1:02:39But you can only do that at one feature class at a time.

1:02:42So it's a little bit of a lengthy process depending on how many checks you have that are pointing to that feature class. So…

1:02:52[Audience question] Could you reference the alias instead of the actual feature class name?

1:02:56Actually, Reviewer's based on feature class, so it will…

1:03:00When you configure your check through the dialog here, it'll list the feature class name.

1:03:10Question right here?

1:03:11[Audience question] I see that you created your sessions in ArcInfo, but can you give this to a technician with ArcView…

1:03:18…and have the reference session?

1:03:19Yes. So that was a good question. He noted that I'm using ArcInfo here, but was wondering if a technician…

1:03:26…if we were to pass this on to somebody else who's using ArcView, could they use it?

1:03:30Actually, Data Reviewer recommends ArcEditor license, but…

1:03:35The reason why is because if you want to go and make the corrections, you need to be able to edit the data.

1:03:41But if you're just doing quality control and just finding the errors, ArcView license is okay too.

1:03:48Another question?

1:03:49[Inaudible audience question]

1:03:52The question is, Can you search for geometric network errors?

1:03:55Did you notice that I opened up that connectivity rules check?

1:04:00Little bit of foreshadowing there for you.

1:04:03So we have a connectivity rules check that will validate your features against your geometric network rules…

1:04:10…if you have your rules defined. Now, if you don't have rules defined, then it will let…

1:04:15…then you won't be able to select anything here. [Audience comment] Okay.

1:04:22And then also, geometry on geometry, some of the examples that he gave on geometry on geometry check…

1:04:27…like looking for hydrants that are not connected to a lateral and stuff, you know, you can use some of the other checks that we have here.

1:04:33[Audience question] I wasn't sure if, like that specific example was actually looking for intersections…

1:04:39…or actually the geometric network…

1:04:41It's just looking for the intersections, so that is a good…yeah.

1:04:46[Audience comment] It's like…one thing that's hard to spot is where we have a service saddle that's on a…

1:04:53…has to be on a main where the main is broken. And sometimes that's a little hard to identify [unintelligible]...

1:05:01…seeing if there's a node there on the main.

1:05:06So, yeah. Any other questions?

1:05:14Alright, so let's go ahead and we'll wrap this up a little bit.

1:05:17So kind of in summary, you know, we talked about ArcGIS Data Reviewer. It is a standard extension to ArcGIS Desktop.

1:05:25And it really, as we've kind of discussed, it really covers the whole quality control process…

1:05:31…from the ability to automate your business rules…

1:05:34…to make that an automated process rather than a manual process and save you a lot of time.

1:05:39We also showed you a lot of the interactive tools that we have out of the box…

1:05:42…that allow you to facilitate the drudgery of doing manual data reviews.

1:05:49The value being that we're writing all these errors all to a common workspace so that we can track them holistically.

1:05:55So these might be errors coming from multiple sources, but we have a single place to store them…

1:06:00…where we can report out, keep track of how we're doing in progress moving forward and correcting our data.

1:06:07You know, the value here is that, because these are automated processes, you can save a lot of time…

1:06:11…so even if you're a one-person shop, this can save you a lot of time through automation.

1:06:15If you're a large team, the value is that you can have QC defined one time, those rules distributed across the team…

1:06:22…and everybody's doing the same QC. It's not this person doing it one way and that person doing it a different way.

1:06:28We have a common framework for doing this work.

1:06:33So lots of resources available for you. There's eval copies obviously available for you to test out.

1:06:39The Data Reviewer poster that I showed a little bit earlier with all those 42 different checks is available for download as well.

1:06:45We have both instructor-led training as well as a couple free classes on the Virtual Campus you might want to take a look at.

1:06:51One is a web course; one is a training seminar that's been recorded.

1:06:55We have the Reviewer Resource Center as well we released at 10, so you'll find things like…

1:07:00…we've got a very active blog that we'll go through in excruciating detail about how you might use Reviewer…

1:07:05…for water, wastewater, for metadata checks, things like that.

1:07:08We also have a number of gallery items you might want to download--video, demos, and all that kind of fun stuff.

1:07:16Questions? We have an alias,, where if you have questions…

1:07:20…you can send e-mails to us and we'll get back to you and let you know how that works.

1:07:25And the other thing to note too, talking about the resource center, is if you visit the resource center…

1:07:30…and some of the other vertical industries there, we're really trying hard to whenever there's an industry model, for example…

1:07:36…like with the water, the Water Utilities Resource Center, we'll build a number of checks…

1:07:41…based on our own SME input on that industry model and publish those out.

1:07:46It's a straw man, a starting point. It's not going to be a hundred percent solution…

1:07:50…but it'll at least get you started and start thinking about how you might want to implement it.

1:07:53So we have the grand plans for, you know, sort of marching forward and also putting QC rules up in other industry areas as well…

1:08:00…so kind of keep an eye out for that in the area of your interest.

1:08:05So we've got a lot of Reviewer sessions going on at UC.

1:08:08So right now we're kind of halfway through; we have a number of demo theaters.

1:08:13We didn't really talk about assessing positional accuracy, so we have a demo theater where we'll be talking about…

1:08:19…how you can assess positional, both horizontal and vertical, accuracies of our data--of your data in demo theater.

1:08:25We have a tech workshop happening tomorrow--or actually, I wrote ahead, so tomorrow morning…

1:08:29…we'll be talking about what's happening at 10.1 if you're interested, first thing in the morning.

1:08:34We're going to rerun this guy tomorrow, and then what do we got…oh, the custom check demo theater.

1:08:38So if you have those 42 checks and they ain't going to work for you, come talk to Shankar about how to develop custom checks…

1:08:46…but still implement them within the Reviewer framework so it's a part of your overall QC process.

1:08:54We also have folks down in the Geodatabase Island; we've got a couple workstations set up.

1:08:58We're also doing wastewater health checks this week, so if you're a water utility…

1:09:04…and you happen to have a sample set of data in your pocket, you can find some time, come down and talk to our guys.

1:09:09If there's time; I don't know if there's a lot of space left.

1:09:11But they'll run through your data with you using the industry checks that we publish out on the water utilities site…

1:09:17…give you some feedback about how that sample data looks.

1:09:22Alright, so a little bit of logistics also.

1:09:24So the session feedback's gone digital this year, no little, little notecards, so when you have a moment…

1:09:30…please avail yourself of this website and let us know how we did. We appreciate the feedback.

1:09:36Any last questions before the end? We good? Got a couple questions right here.

1:09:41[Inaudible audience question]

1:09:47No, you can create it with ArcView.

1:09:50Okay. Any more questions? In back?

1:09:52[Audience question] Early on, you mentioned it can work with large datasets. How do you define a large dataset…

1:09:57…or what do you consider a large dataset?

1:09:59How big is Hong Kong Lands?

1:10:00So the question was, you know, we say "large" datasets. You know, let's talk about how large is large.

1:10:06Michelle, you did some pretty large work with Hong Kong Lands, right?

1:10:10I don't remember how many features was that, but at least recently we were just validating some features.

1:10:16That was like 1.7 million. That's large. But probably anything over…You know, if we just have, at a feature…

1:10:24…just one feature class, I would say over a couple hundred thousand features I would consider large.

1:10:31How big's a batch job?

1:10:34Jay was asking how big are the batch jobs. Batch jobs vary.

1:10:39They can be really big where you have hundreds of checks, even thousands of checks depending on your business rules.

1:10:46Question in back?

1:10:47[Audience question] Does it work with version 10 and 9.3? Do you have those versions on it?

1:10:53So the question is, Does this work with version 10 and 9.3? At…

1:10:58Before 10, Data Reviewer was called PLTS GIS Data ReViewer…

1:11:02…so there's a slight name change, and yes, it's available at 9.3; 9.2, 9.3, 9.3.1, and 10.

1:11:09So at 10, we just have a renaming, ArcGIS Data Reviewer.

1:11:13Question over here?

1:11:14[Audience question] So does Data Reviewer have all the cross-functionality now, or is it separated?

1:11:21So the question is…

1:11:23[Unintelligible audience question]

1:11:28Oh, oh.

1:11:29The other components of PLTS? It's now called Production Mapping.

1:11:36Right. We had a big name change at 10, so some folks are familiar with Data Reviewer…

1:11:41…as a component of Production Line Toolset prior to 10.

1:11:46So at 10, the whole box got shuffled, and now PLTS Foundation is now known as Production Mapping…

1:11:52…and there's some other minor name changes, but Data Reviewer…

1:11:54[Unintelligible audience question]

1:11:58That's why we can change our business cards every time, but it's still a component of Production Mapping.

1:12:04It's also available stand alone. We really didn't talk too much about it in the past about a stand alone…

1:12:08…but it's always been available stand alone for folks who don't need all the other bits and pieces of Production Line Toolset. Question?

1:12:16[Audience question] In one other session, they mentioned in 10.1 that [unintelligible] date and editor stamps.

1:12:26Is there any way to implement that in 9.3.1 now, or is that…

1:12:32Yes, you can, using Production Mapping, or PLTS.

1:12:36[Audience question] So you can?

1:12:37Well, so PLTS has the ability to keep track of feature-level metadata.

1:12:41You can identify what fields you want to store the last editor and the last update fields, and by setting up what we call…

1:12:50Oh, was it the knowledge base tables? Yeah, knowledge base. Yeah.

1:12:53So if you go to Geodatabase Island, there's a girl there, Amber, she's working…I think she's working there…

1:13:00…but she can help you answer that.

1:13:03And the thing to remember is that it's feature-level meta-..

1:13:06What PLTS does is way…it does a lot more than just track the last editor.

1:13:11Essentially, you may have half a dozen different feature-level metadata fields…

1:13:16…and you can have those autopopulate with Production Line Toolset or Production Mapping at 10.

1:13:22Any other questions? Alright, thank you very much.

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ArcGIS Data Reviewer: An Introduction

Michelle Johnson and Jay Cary give an overview of ArcGIS Data Reviewer and its quality control tools that simplify the process of reviewing and maintaining data quality.

  • Recorded: Jul 13th, 2011
  • Runtime: 1:13:30
  • Views: 85621
  • Published: Sep 19th, 2011
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