Explore the data

To better understand trends in permit activity in Montgomery County, you'll create a workbook in ArcGIS Insights and add a dataset of permits issued since 2010. However, before you begin your analysis, it's important to explore your data and understand what it shows and does not show. You'll familiarize yourself with the data's attributes, sort the data by type, and visualize spatial and temporal trends. In doing so, you'll gain context for your analysis and know exactly which questions you still need to ask to find out why growth is occurring where and when it is.

Begin a new workbook

First, you'll sign in to Insights in ArcGIS Online and begin a new workbook. You'll download and add Montgomery County permit data to your workbook for analysis.

  1. Download the Commercial_Permits_since_2010 compressed folder.
  2. Locate the downloaded file on your computer. Right-click the file and extract it to a location where you can easily find it, such as your Documents folder.
  3. Sign in to your Insights Online account.

    To access Insights in ArcGIS Online, your ArcGIS organization's administrator must grant you a license for it. If your organization does not have Insights licenses, you can sign up for a free trial.

    If this is your first time using Insights, the Welcome to Insights window appears with a list of things you can do with Insights.

  4. If necessary, in the Welcome to Insights window, click Skip.
  5. Click the Workbooks tab.
  6. Click New workbook.

    New workbook button

    The Add To Page window appears. In this window, you can choose a dataset to add to your new workbook. You can choose data hosted in your ArcGIS organization, data in a file on your computer, or data from ArcGIS Living Atlas. You will add local data from a Microsoft Excel workbook.

  7. In the left pane, click Upload file.

    Files option

  8. Click Browse my computer. Browse to the extracted Commercial_Permits_Since2010 folder and double-click CommercialPermits2010.xlsx.
  9. In the Selected Data pane, click Add.

    Add Excel sheet to workbook

    The dataset is added to a new card in your workbook. You use cards and pages to organize information in a workbook. Each page can contain multiple cards, and each card can contain a map, chart, or table. You'll rename your workbook, page, and cards with more meaningful names.

  10. On the ribbon, click Untitled Workbook, type Commercial Permits: Montgomery County, MD and add your initials at the end. Press Enter.

    Change workbook title

  11. Click Page 1, type Data Exploration, and press Enter.

    Change page name

  12. In the data pane, point to Commercial Permits since 2010.ComPermits2010 and click Rename dataset.

    Rename dataset button

  13. Type Commercial Permits since 2010 and press Enter.
  14. Point to Commercial Permits since 2010, click Dataset options, and choose Enable location.

    Enable location option

    The Enable location window appears.

    Enable location window

    There are four options for enabling location: Coordinates, Addresses, Geography, and Standard boundaries. You can choose from the four types by clicking the tabs at the top of the window. The default is to use coordinates, which is what you want.

  15. In the Enable location window, for X (Longitude), confirm that LONGITUDE is chosen, and for Y (Latitude), confirm that LATITUDE is chosen. For Spatial reference, confirm that 4326 - GCS WGS 1984 is chosen.
  16. Check Repeat identical features.

    Enable location parameters

  17. Click Run.

    When the Enable location tool finishes, a Coordinates attribute is added to Commercial Permits since 2010.

  18. In the data pane, click the arrow next to Commercial Permits since 2010 to expand its attributes. Drag the Coordinates attribute to your page and onto the Map drop zone.

    Create a map card


    You can also select the field and click the Map button on the ribbon to create a map card.

    A map card titled Card 1 is added to the workbook.

  19. Click a blank area on your page to deactivate the card. Click Card 1, type Map of Commercial Permits, and press Enter.

    Renamed workbook, page, and card

  20. Click the Save button.

    Save button

Add a table and chart

Now that you've added the permit data, you'll explore its contents. Geographic data doesn't only contain information about location; it can also include other attributes not seen on a map. The left pane, called the data pane, lists your data and can also be used to explore these attributes.

To visualize important attributes, you can add them to a new card as a map, chart, or table, depending on what kind of data the attribute contains. Two attributes of interest to Montgomery County are the use code and status of each permit. Use codes describe what kind of building the permit is for, while status describes whether the permit has only been issued or whether all required inspections have been completed. You'll add one attribute as a table and the other as a chart.

  1. If necessary, in the data pane, click the arrow next to Commercial Permits since 2010 to expand its attributes.

    The permit data contains a long list of attributes. Some attributes have self-explanatory names, while others may have names that can be difficult to understand without context. The icon next to each attribute indicates the type of data it contains.

  2. Scroll near the bottom of the list of attributes. Drag the USE_CODE field to the blank area next to the map card, point to the Table drop zone, and release it on Summary Table.

    Table drop zone

    A new card is created. It shows the number of permits for each use code and the total number of permits. You'll sort the table to see which categories have the highest count.

  3. In the table card, click the sort arrows next to the COUNT of Commercial Permits since 2010 column.

    You may need to expand the card by dragging the right handle until the Sort button is visible.

    Sort button

  4. Click the sort arrows.

    The table is sorted from lowest count to highest count, which isn't what you want.

    Table sorted low to high

  5. Click the sort arrows again.

    Now the table is sorted from highest count to lowest count.

    Table sorted high to low

    The most common use code, Business Building, has almost twice as many permits as the second highest, Multi-family Dwelling. The top four use codes together comprise the majority of all permits, so these use codes may be the most important to focus on in your analysis later.

  6. Click anywhere outside the table card to deselect it. Then, rename the card Permits by Type.

    You'll also create a card to show permits by status. There are only four permit statuses: Issued, Finaled, Open, and Stop Work. To visualize the number of permits for each status, you'll create a treemap.

  7. In the data pane, scroll to the top of the list of attributes. Drag the STATUS field to the Chart drop zone and onto Treemap.

    Create a treemap

    The chart card shows the four permit statuses as a treemap, with the size of each status determined by the number of permits.

    Treemap card

    After generating an initial chart, it is possible to update and modify the chart type on the card.

  8. In the Treemap chart card, click the Visualization type button and choose Donut Chart.

    Change from treemap to donut chart

    The card updates to display the permit status as a donut chart.

    Donut chart showing permits by status

    The vast majority of permits are either Issued or Finaled. Finaled permits are issued permits that have also had the requisite inspections performed.

  9. Deselect the card and rename it Permits by Status.

    Currently, your map card only shows permits with a single symbol. However, it's helpful to visualize the spatial distribution of permit attributes. You'll change the map so that each permit's symbol represents its status.

  10. In the data pane, drag the Status field onto the Map of Commercial Permits map card.

    Style map by Status field

    The symbology changes.

    Map symbolized by permit status


    The color of your layer may differ, but the results are the same.

  11. In the upper right corner of the map card, click the arrow next to STATUS.

    Review layer options

    The Layer options window, which contains a legend, appears.

    Layer options window

    The color for each permit status matches the colors in the donut chart. Because the map card and chart card are based on the same attribute, the two cards are linked.

    Next, you'll explore the spatial distribution of specific status types or use codes.

  12. Close the Layer options window.
  13. In the Permits by Status card, click the donut chart segment for the Open status.

    The map changes to highlight permits with the open status.

    Open permits

    All cards are connected, so when you select something in one card, the other cards update to show the same selection.

  14. In the Permits by Type card, click the Townhouse use code.

    Permits issued for townhouses

    As with the donut chart, only townhouse permits are highlighted.

  15. Click the Townhouse use code again to deselect it.

    The map shows all permits again.

Clean up the data

Before you begin analysis of your data, you'll hide attribute fields you don't intend to use, rename fields with unclear names, and filter your dataset to only show permits with the four most common use codes. These changes won't permanently affect the original dataset, but they will make the data easier to work with and understand.

  1. In the data pane, scroll to the bottom of the list of attributes. While pressing Ctrl, click each of the following fields to select them:

    These attribute fields describe aspects of the data that aren't important for your analysis. You'll hide these fields.

  2. Scroll to the top of the list of attributes, click the Dataset options button, and choose Hide selected fields.

    Hide selected fields option

    The fields are no longer listed. You can show the fields again by choosing Show hidden fields. Next, you'll rename some of the attribute fields with shortened or unclear names so that their names are more descriptive.

  3. Scroll to the bottom of the list of attributes, point to the DESCRIPTIO field, and click the Rename field button.

    Rename Field button

  4. Change the name of the field to Description and press Enter.
  5. Rename BLDGAREANU to Building Area and rename DECLVALNU to Declared Value.

    There are other fields that you may want to either rename or remove, but for the purposes of this lesson, these are enough. Next, you'll filter the permits to reduce the number of records in your analysis. As you saw previously, there are four types of permits that comprise over half the total number of permits. Focusing your analysis on only these four types will reduce the amount of data to analyze without ignoring the most important types of development. To remove the other use codes, you'll create a filter.

  6. In the data pane, point to the USE_CODE field and click the Dataset filter button.

    Dataset filter button

    The Filter by USE_CODE window appears. Unchecked attributes will be hidden in the dataset.

  7. Click the arrows button and choose Count: Highest to lowest.

    Choose high to low count

  8. Uncheck Select All to uncheck all attributes. Then, check the boxes next to the four highest count categories (Business Building, Multi-family Dwelling, Commercial Miscellaneous, and Mercantile Building).

    Filter by USE_CODE window

  9. Click Apply.

    The dataset is filtered.

    Filtered table card

    The map card, the table card, and the chart card all change to reflect only permits with the four selected use codes. Instead of more than 11,000 permits, the data displays about 7,500.

  10. Save the workbook.

Visualize temporal and spatial trends

Your data shows permits, but what do these permits say about when and where growth is happening in the county? Your data also contains temporal attribute fields, such as ADDED_DATE, which indicates when a permit was first added to the system. The field has several subfields generated in the workbook to help you interpret and break down the date/time field data by year, month, and even hour. You'll create chart cards for the year, month, and day subfields to visualize patterns in permit activity over time. Then, you'll symbolize your map card as a heat map to see the highest spatial concentrations of permits.

  1. In the data pane, under ADDED_DATE, drag the Year field to the Chart drop zone and create a bar chart.

    Add a bar chart for permit year

    The bar chart is added to the workbook page.

    Bar chart showing permits by year

    The chart shows the number of permits issued each year since 2010. (The year 2017 has significantly fewer permits because the dataset only covers part of 2017.) You can compare the number of permits visually by the size of each bar, or you can point to a bar to see the exact number of permits for that year. The dotted red line shows the average number of permits per year: 939. Although some fluctuation occurs from year to year, most years had similar permit activity.

  2. In the data pane, under ADDED_DATE, drag the Month field onto the same bar chart card.

    The bar chart changes to show the number of permits issued by month.

    Permits by month

    Based on the chart, the highest permit activity occurs in June and July.

  3. Under ADDED_DATE, drag the Day of week field onto the same bar chart card.

    Almost all permit activity occurs on weekdays.

    Permits by day

    Government offices are closed on weekends, so few permits are issued then.

    Another way to view permits over time is to display a timeline of activity, rather than categorize by either year, month, or day. A timeline, also called a time series graph, will emphasize patterns in the total distribution of permit activity over time. You'll remove the existing bar chart and replace it with a time series graph.

  4. In the bar chart card, click the More button and choose the Delete button.

    Delete button

  5. In the data pane, drag the ADDED_DATE field to a blank area of the workbook page and release it on the Time Series drop zone.

    Time Series drop zone


    Because the ADDED_DATE field is a date/time field, the types of cards you can create with it are different. The Time Series drop zone replaces the Chart drop zone.

    The time series graph is added to the workbook.

    Time series chart

    The default card size is too small for a long timeline. You'll resize the card to better visualize the data.

  6. Drag the center-right handle of the time series card until the card is the length of two cards.

    Time series card with arrow showing how to resize it


    The colors in your chart may differ, but the results are the same.

  7. Click outside the card to deselect it. Then, rename the card Permits over Time.

    Permits over Time card


    Point to a specific point on the line to see that point's count of permits.

    With the chart expanded, patterns and outliers are easier to detect. For example, a huge spike in permit activity occurred in mid-2011. What caused this spike? Is it an increase in overall permit activity, or is it mostly an increase in a certain type of permit? You'll group the time series card by use code to answer these questions.

  8. Select the time series card. In the lower center of the chart, locate and click Group by and choose USE_CODE.

    Group by list with USE_CODE field selected

    The time series splits into four distinct lines, one for each of the four major use codes. Only one of the four lines contains the large spike in 2011, indicating that the spike was caused by an increase in a specific type of permit.

    Use Code time series

  9. In the time series card, click the Layer options button.

    Layer options button

    Based on the legend, permit activity spiked in 2011 due to a sharp increase in the number of multi-family dwelling permits issued. This likely means that there was large residential growth in 2011.

    You've investigated some temporal patterns in your data. Next, you'll look at spatial patterns. Are there certain areas in the county that have experienced a relatively high degree of permit activity? Was the 2011 spike in residential permits in a specific location? To find out, you'll change the symbology of the map card to show hot spots, or areas with concentrations of points.

  10. In the map card, click the arrow next to the layer name (STATUS). In the Layer options window that appears, click the Symbology tab.

    Options tab

    The window displays style and symbology options.

  11. For Symbol type, choose Heat Map.

    Symbol type option

    The map card changes to show hot spots.

    Heat Map symbol type

    The heat map displays. Based on how far out you are zoomed, the colors will be more or less intense. The bright yellow areas are where there is a high concentration of permits. The highest concentration areas are in the southeast and northwest corners of the county, which correspond to the major population centers of Germantown and the suburban communities near Washington, D.C. Next, you'll see if the 2011 permit spike corresponds to a specific area of the map.

  12. Close the Layer options window.
  13. In the Permits over Time card, click the spike during mid-2011 to select it.

    Hot spot in Germantown

    Because cards are interconnected, when you select data in one card, it highlights related data in the map. In this case, the heat map changes to show the hot spot in the northwest part of the county, near Germantown.

    Based on your exploration, it seems as though a large spike in residential growth occurred in the area near Germantown during the summer of 2011. Now that you have a better idea of where and when growth is occurring, you know what questions you still need to ask to find out why growth is occurring.

  14. Click the Permits over Time card again to deselect the spike in permits. Save the workbook.

You've created a workbook in Insights and added permit data for Montgomery County, Maryland. You explored the data on a map and through tables and charts. Then, you visualized some of the spatial and temporal trends in the data. Next, you'll perform analysis functions on your data to gain new insight into why development is happening and where you can expect future development to occur.

Analyze patterns

Previously, you explored your data and learned a little about the spatial and temporal trends of permit activity in Montgomery County. In this lesson, you'll move beyond exploration and run spatial analysis tools to answer specific questions that can't be answered by the data itself. In particular, you want to know why permits spiked in Germantown in 2011 and predict where future permit spikes—and, by extension, future growth—are likely to occur.

First, you'll aggregate the points by ZIP Code Tabulation Area (ZCTA). You'll enrich each ZCTA with demographic information and learn more about the demographic conditions that led to such rapid growth in such a short time. Once you determine why growth occurred where and when it did, you'll locate other ZCTAs with similar demographic characteristics to predict future growth.

Aggregate points

It can be difficult or even impossible to run certain analysis functions on a point dataset, especially analysis functions based on spatial demographic data. You'll aggregate the points based on ZCTA, using data from a compressed shapefile that you downloaded in the first lesson. Before you aggregate the points, you'll change the map's symbology back to individual symbols for each point.

  1. If necessary, in Insights, open your Commercial Permits: Montgomery County, MD workbook.
  2. In the map card, open the Layer options pane. On the Symbology tab, change the Symbol type to Location (Single Symbol).

    Update symbology to Location (Single symbol).

    The map updates to display all points using the same symbol.

    Permits symbolized the same

  3. Close the Layer options pane.

    You'll also expand the size of the map card to see the data in more detail.

  4. Drag the lower right handle of the map card until the card covers most of the workbook page.

    To aggregate points by ZCTA, you need both the point layer and the ZCTA layer.

  5. On the ribbon, click Add to page.

    Add to page button

    The Add to page window appears. You previously used this window to add the permit dataset to your workbook when you created it.

  6. Under Add to page, click Upload file.
  7. Click Browse my computer. Browse to the folder that you downloaded and double-click the compressed shapefile cb_2018_us_zcta510_500k_MD.zip.

    The zipped shapefile appears in the Selected Data area.

    Shapefile to be added

  8. Click Add.

    The cb_2018_us_zcta510_500k_MD layer is added as a new item in the data pane.

  9. Rename the layer ZCTA - Maryland.

    Next, you'll use this layer to aggregate permit points.

  10. Drag the ZCTA - Maryland layer onto the map card and release it on the Spatial aggregation drop zone.

    Spatial aggregation drop zone

    The Spatial Aggregation pane appears.

    Spatial Aggregation pane with default parameters highlighted

    By default, the parameters are set to use ZCTA - Maryland as the area layer, the permits as the layer to be aggregated, and the layer style to be based on permit count. These parameters are exactly what you want.

  11. In the Spatial Aggregation pane, click Run.

    The analysis runs. When it finishes, a new layer called Spatial Aggregation 1 is added to the data pane, and the map is updated to be styled by the Count of Commercial Permits since 2010 field.

    Spatial aggregation result layer

    The resulting layer appears to be a point layer, but it's actually a polygon layer with a graduated symbol. Each graduated symbol represents the number of permits per ZCTA. Larger symbols indicate ZCTAs with more permits.

  12. Click a ZCTA feature on the map.

    When you click a ZCTA, a pop-up displays the exact count of permits as well as a comparison bar indicating the average (178), the minimum (1), and the maximum count (765).

    Count of permits in Germantown

    These statistics are also summarized in the info page for each card.

  13. On the map card, click the Flip card button.

    Flip card button

    The card flips over and reveals some basic statistics about the data.

    Card info

  14. After you finish reviewing the statistics, click the Flip card button again to return to the front of the card.

    Although most of the large point symbols on the map are in the southeast corner, near Washington, D.C., there are a few large points in the northwest. In particular, there is a very large circle in the ZCTA located east of Germantown.

    ZIP Code Tabulation Area 398

    This ZCTA has 765 permits, the highest in this area. Additionally, this area geographically corresponds to the hot spot you identified in the previous lesson. This ZCTA is one that you'll focus on when you enrich your layer with demographic data.

Enrich the data

Are there demographic characteristics about the ZCTA that contributed to its high growth? If so, are there other areas with those characteristics that may experience growth in the future? To answer these questions, you'll use the Enrich Data analysis capability, which adds demographic attributes of your choice to your data. Specifically, you'll add Tapestry information to each ZCTA. Tapestry is a summary of many demographic and socioeconomic variables, including age groups and lifestyle choices. It'll teach you more about the types of people who live in your area of interest and help you better understand the reasons why growth happened where it did.

  1. In the map card, in the lower right, locate and click the Action button.

    The map card must be selected for the button to appear.

    Action button

    The Analytics pane appears. It has two tabs. The Spatial analysis tab is the best choice when you know which analysis capability you want to use, while the Find answers tab will guide you to an analysis capability based on the questions you want answered.

  2. On the Spatial analysis tab, click Enrich Data.

    Enrich Data analysis capability

    The Enrich Data tool has two parameters. The first is the layer you want to enrich, which, by default, is your aggregated ZCTA layer (Spatial Aggregation 1). The other parameter is the demographic data you want to add.

    Enrich data

  3. For Select lifestyle and demographic data, click Open data browser.

    The Data Browser window appears. By default, it searches for global data.

  4. For Country of area, verify that United States is chosen.

    Select data region

    Data is organized by type, with categories including population, education, and income.

  5. Scroll down and click Tapestry.

    Tapestry data category

    There are multiple Tapestry variables. Many of these variables provide the count of people or households that correspond to a specific Tapestry category. You want the name of the dominant Tapestry segment for the entire ZIP Code.

  6. Under Popular variables, check 2021 Dominant Tapestry Segment Name.

    As you select enrichment data variables, the Selected Variable counter in the data browser will update.

    Tapestry segment name variable

    You have the option to enrich the data with multiple variables at once, but this variable is enough.


    Tapestry data updates periodically. You can select the most recent data available to you. If the variable doesn't appear in the list of popular variables, you can search for it by name.

  7. Click Apply.

    The Enrich Data pane adds a new parameter, Review data. This parameter contains the selected variable Dom Tapestry Segment Name.

    Enrich Data tool parameters

  8. In the Enrich Data pane, click Run.

    Enrich Data runs and a field is added to the Spatial Aggregation 1.

  9. On the map card, click the arrow next to the Count of Commercial Permits since 2010 layer. In the Layer options pane, click the Symbology tab.

    The symbol for this layer shows the count of permits using graduated symbols. You'll symbolize each ZCTA based on the attribute field with which you enriched the layer.

  10. For Style by, choose 2021 Dom Tapestry Segment Name.

    Change layer symbology style by option

  11. Close the Layer options pane.

    Symbolized Enrich Data layer


    The colors chosen for each Tapestry segment are random. Your map may differ from the example images.

  12. Point to some of the ZCTAs.

    Not only has the symbology changed, but now the Tapestry segment is displayed when you point to a ZCTA. The Tapestry segments have names such as Boomburbs and Savvy Suburbanites. You can look up more information about each segment, including its specific demographic characteristics, on the Tapestry Segmentation help page.

  13. In the data pane, expand Spatial Aggregation 1 and select the Location and Count of Commercial Permits since 2010 fields.

    Select Location and Count of Commercial Permits since 2010 layers

  14. Click and drag them to the map card and drop them in the Add new layer drop zone.

    Drag layers into Add new layer zone of map card

  15. Drag the Count of Commercial Permits since 2010 layer below the 2020 Dom Tapestry Segment Name layer.

    Drag layer below the other

    The layer order updates.

    Layer order changed

    Because the Tapestry layer is transparent, the aggregated point symbols are visible for each ZCTA. What Tapestry segment is dominant for the area 398 where major growth occurred?

  16. Point to the area close to Germantown.

    Tapestry segment for Germantown area

    According to the pop-up, 2D, which is the Enterprising Professionals designation, is the dominant tapestry segment for the ZCTA. Enterprising Professionals tend to live in condos, townhomes, or apartments, and the housing in these segments tends to be new, with 25 percent of the housing built after 2000. This description may explain why the area saw such a rapid increase in permits for multifamily dwellings in 2011. It's possible that other ZCTAs with similar demographic profiles may experience rapid growth in the near future.

    The pop-up displays how many tapestry segments are in the area. In this case, there are 42 total segments and out of that, 9 of them are Enterprising Professionals.

  17. Point to the ZCTA directly north of the area previously identified.

    Several segments surrounding Germantown are also identified as having Enterprising Professionals as the dominant Tapestry segment . However, their number of permits have been relatively low since 2010. The county may be able to anticipate a similar spike in permit activity in this area.

    Again, your data and map colors may vary; Tapestry is updated periodically.

  18. Save the workbook.

    Although Tapestry segments are based on several demographic characteristics, you could also perform this analysis with other variables. For instance, you could determine if there is a correlation between high permit activity and high population growth. Is a young population or a high income level a stronger indicator of growth? You can answer these questions and others with the analysis tools at your disposal. For the purposes of this lesson, however, your results are satisfactory.

Share your work

You've analyzed your data and come to a couple of conclusions about your data. Next, you'll share your results online. Currently, your result layers are temporary datasets stored only in your Insights workbook. Sharing your data will make it easier for county officials to use your data in other ArcGIS applications and communicate key information to the public. In particular, you'll share your work to ArcGIS Online. First, you'll share your enriched ZIP Codes dataset as feature layers that can be added to any web map. Then, you'll share your time series graph of permits issued over time.

  1. In the data pane, if nesessary, expand the Spatial Aggregation 1 layer.

    The layer contains fields for both the count of permits per ZCTA and the dominant Tapestry segment—basically all of the result data you created in your analysis.

  2. Next to the layer name, click the Dataset options button and choose Share.

    Share option

    The Share data window appears. Before you share your data, you'll give it appropriate metadata so people who want to use your dataset will have a better understanding of what it contains.

  3. Change the Title field to Commercial Permits Enriched Count by ZCTA, followed by your initials.
  4. For Tag, type (or copy and paste): Montgomery County, Permits, Planning, Tapestry, and press Enter.
  5. For Description, type (or copy and paste): The top four categories of commercial permits for Montgomery County, Maryland, aggregated by ZIP Code and enriched with the dominant Tapestry segment.

    Currently, you only want county officials to see your data, so you'll share with your organization.

  6. Under Share with, check the box for your organization.

    Share data parameters

    When the county is ready to publish the data publicly, you can change the sharing options so everyone can access the dataset.

  7. Click Share.

    After a few moments, the data is shared. In the data pane, a note is added to the layer to indicate that it has been shared.

    Note indicating that the layer has been shared

  8. Click Open item.

    The layer's item page opens in ArcGIS Online in a new browser tab. You can add the layer to a web map or download it. You can also access the layer through your Content page in ArcGIS Online or the home page in Insights.

  9. Close the item page and return to the workbook.

    You'll also share the time series graph that shows permit activity over time for each of the four major permit types. You can share any card in your workbook by sharing the page it's on. Doing so will also share every other card on the page, but you only want to share the time series graph. You'll create a page with only the time series on it.

  10. Drag the time series graph to the New page button.

    Copy time series to a new page

    A page, called Page 2, is added to your workbook with the time series graph.

  11. Rename the page Commercial Permits over Time.

    Page renamed

  12. Save your workbook.
  13. Click the arrow next to the Commercial Permits over Time page name and choose Share.

    Share time series

    As before, you can add metadata to your data before you share it.

  14. Change the following parameters:
    • For Title, type Commercial Permits over Time followed by your initials.
    • For Tag, type (or copy and paste) Montgomery County, Permits, and Planning. Press Enter.
    • For Description, type (or copy and paste) Time series chart of the top four use codes for commercial permits in Montgomery County, Maryland.
  15. For Share with, check the box next to your organization.

    Set time series page parameters

  16. Click Share.

    After a few moments, your page is shared. You can access your page as a read-only item in Insights, open its item page, or copy an <iframe> to embed in HTML code.

  17. Under View your shared page, click Go.

    View your shared page

    Another browser tab appears. It contains a simplified view of the page you shared. Your chart retains its interactive functionality, and you can point to specific parts of the chart to learn more. Because the page has only one element, it may lack context. You can also copy the <iframe> code and use it to embed the time series into a web page,ArcGIS StoryMaps, or other HTML code.

    Published time series chart

In these lessons, you used Insights to explore and analyze permit data for Montgomery County, Maryland. You answered questions about your data's spatial and temporal trends and located areas of the county with rapid growth. You compared your findings with demographic data, came to conclusions about the possible causes of growth, and even predicted an area that may experience similar growth in the future based on shared demographic characteristics. With Insights, you can perform a similar workflow on any of your data to better understand what it contains and what questions it can answer.

You can find more lessons in the Learn ArcGIS Lesson Gallery.