Latest Contributions by KevinPriestley

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.  ... View more

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.  ... View more

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. ... View more

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.       ... View more

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. ... View more

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. ... View more