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Some really great questions here! Let me try and answer them: A. Record geometries are generated as the union of parcel feature geometries associated with them. As long as you have your parcel features correctly assigned to a record (e.g. your parcel features have a GUID value in their Created By Record field that corresponds to the correct record feature), you can use the Build Extent editing tool or the Build Parcel Fabric GP tool to update record geometries according to the updated parcel geometries. FYI - the Build Extent editing tool runs on a maximum of 1,000 features in the active map view. If you want to update more than 1,000 features or features outside of the active map view, then you should use the GP tool. B. Historic parcel features are created automatically when performing record-driven parcel edits (here is a good video introduction to these workflows). In the database, historic and active parcel features are stored in the same parcel type feature class (lines or polygons). Pro's Parcel Fabric uses a definition query that points to the Retired By Record field to determine whether a parcel feature is active or historic. Now, I think some of the other bullet points in your post point to a really interesting question related to adjustments, alignment, and topology in the parcel fabric: by default, least-squares adjustment tools in the parcel fabric use measurements for both active and historic parcel features to find residual measurement errors on parcel line features. In other words, adjustment tools will affect both active and historic parcels. Alignment tools and topological editing workflows behave similarly. If your question is how to preserve the original data inputs that contain misalignments, you could utilize some other strategies for doing that: exporting, appending, or using Copy Lines To to add new features to a different feature class (or parcel type if you need the benefits of the parcel fabric for managing your data). C. The best free resources can be found in ArcGIS Pro Parcel Fabric Videos Place on Esri Community and through the Parcel Fabric Meetup. There you'll find all kinds of great video resources to help you learn about many different components of the parcel fabric. The Meetup can help you stay up to date on all things parcel fabric. Esri Training has some free resources, and also offers a 3-day instructor-led course on the Parcel Fabric where you build a fabric from the ground up. D. Records exist as the digital representation of legal descriptions in the GIS. What you're describing in this example is a quality-driven edit (here's a helpful video on the topic) updating parcel feature geometry to ensure connectivity or spatial accuracy. For quality-driven edits, you do not need to create a record. If you want to add notes about a quality-driven edit, the first idea that comes to mind is to add an attribute field where you can document changes you've made. Selection tools and the Attributes pane could then be used to update attribute values for features you've modified at one time. Others might have a better idea for handling this kind of process.
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03-05-2024
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A couple clarifying questions: 1. Are you seeing this issue after you validate topology or align parcels? 2. What parcel types are you using to manage subdivisions, parcels, and easements? 3. What version of Pro are you using? The Align Parcels tools can modify the geometry of features in multiple parcel types to prevent misalignments--for example, to prevent the emergence of gaps between rights of ways and lots when you align new lot parcels to existing lot parcels. If you store easements in their own parcel type, you might try turning off the editability of the easements group layer, then running alignment. If that's not possible, you might try working with the Align Parcels tool to manually add/delete anchor points and links to ensure the right features move during the alignment.
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02-22-2024
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Without knowing more about your data structure, what I imagine is happening is that the easements and parcels are being added to the fabric in the same parcel type. Seeds get created when 3 criteria are met: parcel lines form enclosures; parcel lines belong to the same record; parcel lines are stored in the same parcel type. Here is an example from testing: 1. The red lines represent a parcel. The purple lines represents an easement crossing the parcel. The lines for the parcel and the easement form enclosures, belong to the same record, but are stored in different parcel types: 2. Seeds are created for both parcels: 3. Build Active expands the seeds into parcel polygons according to their parcel types, while keeping them in the same record: So the question becomes how to handle these situations when you're adding data to the fabric. If you're using manual entry (aka the Traverse tool) you could create a traverse for the parcel lines, then a second traverse for the easements. If you have the linework all ready to go (from a feature class), another strategy would be to create attribute fields that allow you to delineate the parcel lines from the easements: assign 0s and 1s to two attribute fields, one field that tells you whether a line is a parcel line; the second field to tell you whether a line is an easement. After creating the attribute fields and assigning values to them, you can use Select By Attributes in combination with the Copy Lines To tool to generate seeds in the parcel type for parcels; then again to generate seeds for the easements parcel type. Hopefully that helps!
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02-20-2024
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Have you ever wondered how to configure a network dataset to help answer questions like how many people live within a 15-minute walk of destinations? In this video, we learn about strategies to configure travel modes, costs, and restrictions in a network dataset built from publicly available data. This video was recorded with ArcGIS Pro 3.1 in June 2023.
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06-30-2023
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Have you ever wondered how to create your own network dataset? Network analysis in ArcGIS Pro allows you to answer questions about proximity between places using transportation networks as constraints. In this video, we learn about strategies to create a network dataset using publicly available data. This video was recorded with ArcGIS Pro 3.1 in June 2023.
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06-16-2023
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Image showing descending computer code in multiple colors.Sometimes, answers to data quality questions are readily available. We can discuss trustworthy data providers, offer ideas for data processing or cleanup workflows, describe metadata, or reference industry-standard datasets. However, when we think about this question a bit deeper, we often find it difficult to answer. The question, where do I go to find good data is hard to answer because it depends on an entirely different set of questions related to our project: what do we want to do with the data, how do we want to present it, to whom will we present it, when do we need to finish it, etc. In short, the answer to these questions depend on project considerations: we need to map out a vision, scope, budget, audience, and time for our project before we dive into the data. Let us assume we have answers to project-level questions and turn our attention to principles that guide our search for data, especially how to use metadata and choose data that meet our needs. Data as material ArcGIS Pro is a toolbox that gives us an amazing array of tools to perform operations on and with data. In other words, if our GIS is the tool, then data is the material we work with. The geoprocessing, editing, and cartographic design tools we have in our GIS let us create, modify, modulate, and represent data in unique ways that allow us to tell a story about spatial phenomena. The goal is to use these data to inform decisions we make for ourselves, our organizations, and the broader world. Clip art image depicting a hammer on top of a wrench. At the same time, not all data is created equal. Sometimes, the dataset we need does not exist. At other times it might be the wrong format or out of date. Maybe it contains the wrong attributes or covers a different location than we need. We might find a dataset from an excellent data provider, and later discover that it contains use limitations that prevent us from using it to answer our spatial question. In this sense, data can be thought of as an analog to the materials a carpenter works with: as the wood and fasteners and glue that together comprise an object such as a table. One mistake that we make as GIS analysts is to gather any material that could be relevant to a given project without identifying why we need it, how it will be used, and what purpose it serves. It would be as if a carpenter runs to the hardware store to buy wood and glue and nails and screws of all different sizes, types, and shapes without understanding what their client wants. Project Considerations Image showing an audience seated at a conference.First and foremost, when we search for data we have to understand the vision, scope, budget, audience, and timelines involved for our project. What will we deliver? Who will we deliver it to? Do we have financial constraints that limit the data we can buy? Do we have short deadlines that limit the amount of processing or editing we can perform? How will we maintain our deliverables after the project ends, or if we should maintain our deliverables at all? By finding answers to these kinds of questions, we set ourselves up to streamline data collection and clean-up processes. Now, because there are so many data providers across sectors and industries, this post will not dive into specific data providers. I assume that you know where to find the data you need to work with. Instead, we will use the following scenario: we have identified two datasets we could use in our project. They come from providers we trust, but we still need to know which one to use. Check the Metadata No matter what the data is, we should always look at the metadata record first. A good metadata record will tell us a ton of information: when was the dataset created; when was it last updated; what’s the spatial reference; how accurate is the dataset and at what level of detail; does the data have legally binding use limitations; and what attributes does it have. Using metadata records helps to narrow down our search for data and understand how the data will contribute to our project. Let’s say the metadata on both of our datasets has good attribution and we think it would be possible to use either one. What do we do next? Use the Tools ArcGIS Online and ArcGIS Pro have a ton of amazing tools that can give us a more granular understanding of our data itself. One of my personal favorites is Data Engineering in ArcGIS Pro. Data Engineering gives us the power to generate summary statistics for attribute fields, create charts to visualize the distribution of our data, update map symbology based on fields we select. Data Engineering lets us perform a series of geoprocessing operations to prepare data, such as eliminating unnecessary fields or creating and calculating new fields. With Data Engineering and geoprocessing tools, we can find whether our datasets have different feature counts, if they have missing or null values in their attribute tables, if their summary statistics greatly differ: for example, if two road network datasets purport to describe the same geographic area, the sum of feature lengths should be the same. No matter what the tools tell us, it is up to us to make the decision. If our datasets remain comparable after using tools like Data Engineering, how do we choose? Diagram the Decision In searching for data, we often make tradeoffs. Sometimes we need to trade accuracy for availability. Sometimes we need to trade age for attributes. Maybe we can find the exact dataset we need, but it is tabular rather than spatial. Data considerations that guide our search for data include the type of data we need, age, accuracy, attributes, source, and level of detail. We also need to know availability (e.g., use limitations) and whether it covers our area of interest. That is a lot of information to juggle, especially when comparing more than two datasets! Personally, I am a visual learner. It is probably why I am drawn to cartography: maps allow me to understand places without using words. Writing my options down to make a choice—as in a pros and cons list—do not always work to help me arrive at good decisions quickly. Instead, I find that making simple, color-coded visuals helps me a lot. Here is a grid I used to compare datasets for a recent project: A simple table that depicts data considerations, project needs, and datasets. Under project needs, the creator added what they need for the project. The dataset comparisons are color coded to make selecting a dataset for a project easier. The Data Considerations column helps me identify important characteristics of my data that I can find in a metadata record. My Project Needs column helps document the conditions I need each dataset to meet. And the remaining columns help me determine whether the datasets I am considering will work for my project. A green fill color indicates the dataset meets my needs, red shows it does not, and yellow shows that it does partially. Dataset 3, for example, was recent, freely available, and had the right attributes. But it did not have had the level of detail or accuracy of the 3 other datasets. Parting Words Earlier, I wrote that not all datasets are created equal. It’s also true that not every dataset is perfect. Understanding imperfections in our data is critical for GIS analysts. None of the datasets in the grid above allowed me to perform the work I needed to do, exactly as I envisioned it. To complete my project, I decided to use imperfect data. My choice to use one imperfect dataset instead of another affected the results of my work, and the way people perceive the spatial phenomenon that my work describes. A lot of people look at GIS, spatial data, and data science as prescriptive remedies to all kinds of questions about modern society. And while GIS can—and should—absolutely inform decisions about a whole host of real issues and problems, the products we create are only as good as their constituent elements: the data and the tools used to process it. A carpenter could use screws or glue or nails to affix a tabletop to its legs, but the results of that choice will have a significant impact on the stability of the table. A GIS analyst can perform edits or analyze a dataset in all kinds of ways, with good or bad data and no matter what, we will get results. GIS is simply a tool that acts on and works with material: data. So it goes with GIS: garbage in, garbage out. For further training on finding and using GIS data in a project, check out Esri's Preparing Data for GIS Applications course: https://bit.ly/3NQHMQB.
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05-12-2023
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Have you ever wondered how to work with metadata for ArcGIS items? Metadata helps members of your organization or your audience know information about data, such as quality, use limitations, and attribution requirements. In this video, we use ArcGIS Hub to find and download data, then ArcGIS Pro to add and edit metadata on a feature class we create. This video was recorded with ArcGIS Pro 3.0 in April 2023. For further training on metadata, check out Esri's Preparing Data for GIS Applications course: https://bit.ly/3NQHMQB.
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05-08-2023
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ArcGIS Pro as a toolbox ArcGIS Pro is a toolbox and inside this toolbox is an almost limitless set of tools. Like the way a set of carpenter’s tools are designed to work with and manipulate materials, the material we work with in ArcGIS Pro is data. We can alter the appearance of data using symbology, visibility ranges, definition queries, labeling, masking, and more. We can alter data itself using geoprocessing and editing tools. A person drilling a screw into wood.Because we can do so much with and to data as GIS users, one common mistake we make is to throw all the data we can find into a map. I have done it as many times as anyone, where the first thing I do is find and download as much data as I possibly can when I start a project. At that point, I step back and wonder, what am I making? It would be as if a carpenter went to the hardware store and bought a bunch of mismatching fasteners, wood cut at random lengths, and glue. What is the carpenter making? Who are they making it for? Why are they making it? How will they build it? Focusing our GIS Projects To do this most effectively, I argue that we need to rough out a project before we start the work on it. We need to identify what we want to make, who we make it for, how we do and do not want it to be used, and any constraints we face so that our products and deliverables work. We can focus our efforts using 5 project considerations: vision, budget, scope, audience, and time. Vision A carpenter decides that they want to make something. They have all the tools, but they need to have an idea. What do I want to make? What can I do with it? Why do I want to make it? They should be able to answer these three questions in a single sentence: for example, “I want to make a table so that I can eat a family meal more comfortably”. A person looks out upon a city skyline at dusk.As GIS users, we can apply the same questions to rough out GIS projects. Answers might be, “I want to make a web map that shows car crashes so I can design safer streets,” or, “I want to make a database that stores parcel information for my organization to use,” or, “I want a printed map of potential locations to open a new business”. When we answer these questions concretely, we narrow the number of tools we need for the project. We reduce the number of potential products we will make. We simplify the search for data. Budget In a perfect world, we could make whatever we wanted, whenever we wanted it, and cost would be no object. In this world, our projects and products face constraints, budgetary constraints being chief among them. A person holding a magnet that attracts money. Unfortunately, the real world forces us to confront budgetary constraints.And if we put non-financial resources under the umbrella of “budget” we can also start to picture the technical, infrastructural, or staffing constraints on a project as well. Do we need to buy server space, new computers, software, add-ins, licenses and so on? Do we have adequate staffing to create and maintain the project? Do we need to provide training on new technologies or workflows? Can we afford to do all these things? Can we afford to do some of these things? If someone asks a carpenter to build them a table, two of the first questions the carpenter will ask are: what kind of table do you want and how much are you willing to spend? In other words, the carpenter asks, “What is your vision?” and “What is your budget?” Scope To put it simply, scope defines the stuff we care about and the stuff we do not care about. In defining the scope of a project, we start to identify how we can achieve our vision within constraints. A woman asks a question in a meeting.For example, once they describe a vision and set a budget, the carpenter and their customer will start to define how large the table should be, how it should look, what kind of wood should be used, and so on. Within that scope, the carpenter can meet the customer’s expectations and find opportunities to add their own embellishments, their own style, to the table. In GIS, we can narrow down the set of materials we work with when we define a geographic area of interest, identify data providers we want to use, and outline the workflows or processes we will use to realize our vision. From there, we can create beautiful maps, apps, dashboards, hubs, and more. We are not here to investigate everything, because everything often gets in the way of the one thing we want to make. Time "When do you need your table,” the carpenter might ask their customer. Deadlines are probably top of mind when you think of “time” in relation to a project. We live in a fast-paced world where information updates constantly, so delayed project deliveries might mean making decisions with imperfect or outdated information. But beyond deadlines, work in GIS also involves what I would call the temporality of a project: the relationship of our project to time. A clock and a calendar with a circled date.On one hand, incorporating temporal components in a project adds considerable depth and richness for product end-users. On the other, temporality adds an immense amount of complexity for GIS users and organizations. Let us use a simple, concrete example. We just came to an agreement about the vision, budget, and scope of a new project we want to initiate. We want to create a public web map of population density in New York City. We can only afford to use public data available from the United States Census Bureau, and we want to make this information available as soon as possible. To accomplish this goal, we could use ArcGIS Pro or ArcGIS Online to access decennial Census data. If a population density field does not exist, we will have to add and calculate a field to generate the necessary attributes. Then we can symbolize and share our data. But if we want to know how population density in New York City has changed since 2000, we must ask and answer all kinds of additional questions, because space and data that describes it change over time, too. Census boundaries change. Data definitions and categories change. We will likely need to expand the scope of our project in such cases: not only will we need to perform extra processes and workflows to our data, but we will have to spend more time understanding the variegated terrain of the data. Common temporal questions we might ask of our project include: How did data categories, definitions, and boundaries change? What processes or workflows will we use to resolve temporal differences in our data (e.g. apportionment)? When working with economic data, how do we adjust for inflation? Does it make sense to generalize data or resample rasters to make temporal comparisons easier? Audience A crowd of people on a busy sidewalk. Our audience may be a large group.Unlike the carpenter, whose audience self-selects on a project-to-project basis, we often create GIS products with an amorphous understanding of “audience”. We tend to think of an “intended audience” or a group of people we think would be interested in our product. Sometimes the audience is the public, a small group of stakeholders, or subject matter experts. Sometimes the audience is composed of members within our organization. Each potential audience has different ways of interacting the products we create. Each has different familiarity with maps and GIS concepts such as spatial statistics or raster analysis. A person looking carefully at a map. Our audience may be small, even a single person.Who is the audience? How will they use this product? What story will this product help us tell? Considering our intended audience will help us decide how much specificity or level of detail we must incorporate into our project, the kinds of technologies through which to make our product available, determine accessibility requirements, symbology and design details, narratives, and more. I encourage you to document and record your project considerations before you go out and download every dataset you can find. That way, you can assess whether unforeseen spatial relationship or a specialized web app meets your overarching vision. Although it will take more time up front, you can save yourself a lot of headaches down the line. On the other hand, if you make these considerations too rigid, you may not be able to meet expectations, yet you cannot pick the project up from an earlier moment. You get stuck and resources waste away. To get unstuck, I suggest using these project considerations as guideposts. Check in with yourself or your team regularly. Make sure the vision still makes sense. Make sure the scope is still correct. Make sure you can create what you want for the audience who needs to see it. In other words, measure twice, cut once. For further training on preparing GIS data, check out Esri's Preparing Data for GIS Applications course: https://bit.ly/3NQHMQB.
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05-01-2023
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See how the records feature class represents recorded legal descriptions of property and how relationship classes and attribute rules link the records feature class to parcel polygons and lines. I'll show you the steps to add existing parcel polygons and lines to a new record. This video was recorded with ArcGIS Pro 2.7 in July, 2021. For further parcel data training, check out Esri's Working with Parcel Data in ArcGIS Pro course: https://bit.ly/3OtMMc9.
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06-25-2021
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I'm a bit late to answering your question, but have you tried to use alignment tools/workflows listed here: Alignment methods—ArcGIS Pro | Documentation Align parcels in a workflow—ArcGIS Pro | Documentation
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10-30-2020
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One clarification from my above post. With line data, you would use seeds to create polygons in the parcel fabric. With polygon data, you can use the Append and Build Parcels tools, which will create parcel fabric polygons, lines and points. Records exist for a few reasons: one, to link individual parcels to their corresponding recorded legal description; two, to link parcels with a parcel type in the fabric, and; three, for conformity with parcel fabric topology rules. Records can be created (and persisted) without a geometry--in other words, we can add a new record to the fabric called "New_Subdivision" without associating it to vector data. To that end, "the record footprint or polygon matches the cumulative geometry of all the parcels associated with it." Without knowing more about your particular situation, here's a hypothetical example: Let's say we have a subdivision that's being installed in multiple phases, yet the entire development has the same record on a legal description with the county recorder. The first phase is complete and has been added to our parcel fabric, and now we need to update the fabric with polygon data for the second phase of development. Because the legal record on file with the county recorder is the same for both phases, we want to add the parcels of the second phase to an existing record. In this example, we would: append second phase polygons to the parcel type, build parcels; update COGO (if needed) assign parcels to the record, validate and fix topology errors (if needed) Once we have assigned parcels to a record--in this case the same record as the first phase of development--the record feature will change in size to include the second phase of the development. Are you migrating your fabric from an ArcMap fabric? If so, there's a slightly different workflow: Upgrade an ArcMap parcel fabric—ArcGIS Pro | Documentation Here are a couple more resources about the parcel fabric data model and parcel records: Parcel fabric data model—ArcGIS Pro | Documentation Create parcel fabric records—ArcGIS Pro | Documentation Enable the parcel topology—ArcGIS Pro | Documentation
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10-30-2020
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In your original post, I think the step you've missed is to link your polygons to a parcel type in the parcel fabric. Going forward, you might consider this workflow: add parcels to a parcel type, create the parcels from seeds, validate topology errors, resolve validation errors, and then perform your quality edits. If you want to add polygons to an existing parcel type, you can do so using the Append tool. Unless you need to make some major edits so your parcel polygons match recorded legal descriptions, editing should come last. That's because quality-driven edits (Adjusting boundary lines, aligning parcels with better aerial imagery, etc.) do not affect the parcel record or result in the creation of historic parcels. You will also have parcel topology applied, which can help solve duplication/overlap issues while validating and editing. Here are some resources for this workflow: Append data to the parcel fabric—ArcGIS Pro | Documentation Build parcels from polygons—ArcGIS Pro | Documentation Build parcels from lines—ArcGIS Pro | Documentation Parcel Fabric: Migrating and administrating parcels with ArcGIS Pro Video | Esri (1 hr 10 min)
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10-29-2020
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