Find the meeting locations of a network of associates

In this lesson, you're a law enforcement officer who has identified a person of interest in a recent crime. Because the crime was too substantial to be carried out by a single person, you want to identify the individual's network of associates. To do so, you'll analyze the meeting locations of the person of interest based on the location of their cell phone. Then, you'll pinpoint individuals with whom the person of interest is interacting. You'll perform the analysis in ArcGIS AllSource, intelligence software for investigative geospatial and link analysis.

This lesson was last tested on July 14, 2023, using ArcGIS AllSource 1.0. If you're using a different version of ArcGIS AllSource, you may encounter different functionality and results.

Requirements

• ArcGIS AllSource

Find the meeting locations of a network of associates

Add the data

You'll start by reviewing a fictitious dataset for the area of interest: Djibouti, the capital of the Republic of Djibouti, in the Cape of Africa. This data contains the movement of the cellular devices and their owners throughout the day that the crime was committed.

First, you'll download the data and add it to a project in ArcGIS AllSource.

Note:

All of the data used in this tutorial is fictitious and designed for educational use only.

1. Download the Camp Lemonnier Intelligence Data.

   A zipped folder named Camp_Lemonnier_Intelligence.gdb is downloaded to your computer. The .gdb extension means the folder contains a geodatabase, which is a folder format for storing geographic data.

2. Extract the zipped folder to a location you can easily remember, such as your Documents folder.

   Next, you'll create a project in ArcGIS AllSource and add the data to it.

3. Start ArcGIS AllSource. If prompted, sign in using your licensed ArcGIS account.

   Note:

   ArcGIS AllSource is an optional product that can be installed when you install ArcGIS Pro. To learn more about installing ArcGIS AllSource, go to the Install ArcGIS Pro documentation page. If you don't have ArcGIS Pro or an ArcGIS organizational account, see options for software access.

   When you start ArcGIS AllSource, you're given the option to create a new project or open an existing one. If you've created a project before, you'll see a list of recent projects.

4. Under Blank Templates, click Map.

   

5. For Name, type Find Meeting Locations Project. Click OK.

   The project, which will contain all of the maps and data for this workflow, is created. Because you chose the Map template, the project includes a blank map.

   You'll add cell phone data from the geodatabase that you downloaded.

6. On the ribbon, click the Data tab. In the Add group, click the Add Data button.

   

   The Add Data window appears. You can add data from the project, your portal (ArcGIS Online or ArcGIS Enterprise), or your computer.

7. Under Computer, browse to the location of the extracted geodatabase that you downloaded. Double-click Camp_Lemonnier_Intelligence.gdb.

   

   The geodatabase contains three feature datasets: Administrative_Data, Cell_Phone_Data, and Vehicle_Data. You want to analyze cell phone data.

8. Double-click Cell_Phone_Data.

   

   A feature dataset contains multiple feature classes. Feature classes are collections of geographic features (such as points, lines, or polygons) that can be added to a map. The Cell_Phone_Data feature dataset has nine feature classes, but you'll only use one, Cell_Phone_Data_All, for this analysis.

9. Click Cell_Phone_Data_All to select it.

   

10. Click OK.

    The feature class is added to the map. The map zooms to the extent of the data.

    

    Note:

    Your layer may have a different color than the example images. The color will not affect the workflow.

    The layer includes a large number of points (more than 1 million) concentrated in and around the city of Djibouti in Djibouti, Africa. The area of interest includes an American military base named Camp Lemonnier. In this scenario, you're a law enforcement officer situated at Camp Lemonnier. Your goal is to identify the associates of a suspect in a recent crime affecting the camp using the information collected in these points.

    Each point represents the location of a cell phone at a certain point in time. Multiple points may correspond to the same cell phone as it moved throughout the day. You'll learn more about the layer by opening its attribute table.

11. In the Contents pane, right-click All Cell Phone Data and choose Attribute Table.

    

    The attribute table appears. Attributes are textual or numeric data associated with each feature. In the table, each row represents an individual feature, while each column represents an attribute field.

    

    This table includes fields describing the shape, latitude, longitude, and unique ID (OBJECTID) of each cell phone record. It also has a Time field, which has the date and time that the cell phone's location was captured, and a POI (person of interest) field, which identifies the person to whom the cell phone belonged. In a real dataset, the POI field might include the person's name or phone number, but because this data is fictitious, a number was used instead.

    In the example image, the first four cell phone records all belonged to the same person, identified as Person-102. These records were taken on September 9, 2017, with the first record occurring at 10:19 p.m. and the fourth at 11:44 p.m.

12. Close the table.

Enable time settings

To find the meeting locations of POIs based on their cell phone records, your layer must be time-enabled. From exploring the attribute table, you know that your cell phone data has a time field. You can use this field to enable time settings for the layer.

1. In the Contents pane, double-click All Cell Phone Data.

   The Layer Properties window appears. In this window, you can set many settings relating to the layer. First, you'll set whether the layer has a single time field or start and end time fields. Your data has only one time field.

2. Click the Time tab.
3. Under Filter using time, select Filter layer content based on attribute values.

   

4. For Layer Time, choose Each feature has a single time field.

   

   Next, you'll choose the time field from the list of attribute fields.

5. Confirm that Time Field is set to Time.

   Note:

   For a time field to be used to enable time settings, the field must follow certain rules. If you're performing this workflow with your own data and have trouble enabling time settings, try converting the time field into a date format.

6. Click OK.

   The layer is time-enabled. A timeline appears at the top of the map. When you point to the timeline, it shows the earliest and latest dates for the data.

   

Find meeting locations

Now that your data is time-enabled, you're ready to perform the analysis to find the meeting locations of POIs.

1. On the ribbon, click the Analysis tab. In the Tools group, in the Movement group, click Find Meeting Locations.

   

   The Geoprocessing pane appears. The pane displays the Find Meeting Locations tool. The tool requires several parameters. First, you'll choose the input dataset that you want to analyze.

2. For Input Features, choose All Cell Phone Data.

   Next, you'll choose the name and location of the two output feature classes that the tool will create. One of these output feature classes contains polygons showing the extent of the meeting areas that the tool identified, while the other contains points representing the centers of the meeting areas. These centers are known as centroids.

3. For Output Area Features, delete the text and type Meeting_Areas. For Output Point Features, delete the text and type Meeting_Centroids.

   

   The final mandatory parameter is the name field for the input features. When you looked at the attribute table, you saw the POI field, which identifies the person associated with each cell phone record. You'll use this field.

4. For In Features Name Field, choose POI.

   

   The tool also has two optional parameters that are already filled in with default values. The Search Distance parameter sets the maximum distance that a series of cell phone records by the same POI (known as a track) can move before it is no longer consider part of a meeting. The Minimum Loiter Time parameter sets the minimum amount of time that a track must be within a search distance before it is considered to be dwelling.

   If a track exceeds the search distance in less time than the minimum loiter time, the track is classified as traveling, which means the POI is not stationary long enough to be part of a meeting. Basically, the tool wants to find POIs who stay within a set area for a certain amount of time.

   Increasing the search distance and decreasing the minimum loiter time will return more possible meeting locations, but these meeting locations may be less accurate because POIs may simply be traveling through an area instead of meeting there. Decreasing the search distance and increasing the minimum loiter time will return fewer possible meeting locations, but POIs are more likely to have been meeting there, as they spent more time across a smaller distance.

   For this workflow, you'll use the default values of a 100-meter search distance and a 10-minute loiter time.

5. Click Run.

   Note:

   Because your dataset has a large number of features (more than 1 million), the tool may take several minutes to run.

   The tool runs. When it finishes, a notification appears at the bottom of the Geoprocessing pane.

   

   Tip:

   To learn more about the analysis, including how long the tool took to run, click View Details.

   Additionally, the Meeting_Centroids and Meeting_Areas layers are added to the map and the Contents pane. It may be difficult to see the output layers because of the number of points in the original layer.

6. In the Contents pane, uncheck All Cell Phone Data.

   

   The layer is hidden on the map, leaving only the output layers visible.

   

   Even with the cell phone data hidden, the centroids cover most of the meeting areas.

7. On the map, zoom in toward the Djibouti label until you can see some of the meeting areas.

   

   By default, the centroids are styled based on the duration of the meeting (in seconds). Lighter-colored centroids indicate shorter meetings, while darker-colored centroids indicate longer ones. The meeting areas are styled based on the total number of meetings that occurred in the area, with darker areas having more meetings.

   Based on the style of the centroids and the areas, you can determine based on visual inspection where two POIs loitered in the same area for a significant amount of time. Next, you'll look at the tables for both output layers.

8. In the Contents pane, right-click Meeting_Centroids and choose Attribute Table.

   The table appears.

   

   For each meeting, the table lists the meeting duration in seconds, the time and date the meeting started and ended, the name of both participants of the meeting (Participant 1 and Participant 2), a unique identifier for the meeting, and a unique identifier for the meeting area.

   Based on this information, you can filter the data to only show meetings involving a specific POI or meetings that took place in a specific meeting area. Later, you'll look more closely at the data to see who met with the POI suspected of committing a large crime in the area. That way, you can narrow down the suspect's network of associates.

9. Close the table. Open the table for the Meeting_Areas layer.

   

   This table lists the total unique POIs and the total meetings in each meeting area, as well as the maximum, minimum, and mean (average) meeting duration in seconds. It also lists the start date of the first and last meeting recorded in the area, the area's size, and the area's unique identifier.

   Having information on meeting areas is useful for identifying a network of criminal associates who use the same area to meet with one another, even if they meet at different times and with different members of the organization.

10. Close the table.

Link a suspect to their associates

For the purposes of this scenario, you've already identified a suspect in a recent crime: Person-494. You want to use the meeting location information you've gathered to find out who met with this person, as they may be accomplices. To do so, you'll create a link chart.

A link chart is a nonspatial way of looking at relationships in data. Link charts contain two main components: entities and relationships. Entities (also known as nodes) are people, places, or things, and are traditionally displayed as point features on a map. Relationships (also known as links) are the lines that connect entities in the chart. You can draw relationships based on shared field values in an attribute table or add links manually.

1. On the ribbon, click the Analysis tab. In the Link Analysis section, click New Link Chart.

   

   The Link Chart view appears, replacing the Map view. The link chart is empty by default. You'll set the entities that you'll connect in the chart.

2. On the ribbon, on the Link Chart tab, in the Add group, click Entity Type.

   

   The Add new entity type from layer window appears. For your entities, you'll use the meeting centroids layer. When you looked at the attribute table, you saw the Participant 1 and Participant 2 fields, which listed the two POIs that were meeting. You'll use these fields to define your entities.

3. For Choose layer, choose Meeting_Centroids. For Choose field(s) for entities, choose participant_1.
4. For Enter name for entity type, type POI1.

   

   You'll leave the other parameters set to their default values.

5. Click OK.

   Meeting centroids are added to the link chart, corresponding to each unique POI in the Participant 1 field. Next, you'll add entities corresponding to the Participant 2 field. The bottom of the Link Chart view lists the number of entities (nodes) and relationships (links). There are 368 nodes and 0 links.

6. On the ribbon, in the Add group, click Entity Type.
7. In the Add new entity type from layer window, set the following parameters:
   • For Choose layer, choose Meeting_Centroids.
   • For Choose field(s) for entities, choose participant_2.
   • For Enter name for entity type, type POI2.
8. Click OK.

   More nodes are added to the link chart. There are now 734 nodes. Next, you'll create relationships between POIs who met.

9. On the ribbon, in the Add group, click Relationship Type.

   

   The Add new relationship type between entities window appears.

10. For Choose source entities, choose POI1. For Choose target entities, choose POI2.
11. For Enter name for relationship type, type Meeting.

    

    Next, you'll set the key fields. Key fields are the fields in the attribute table that can be used to connect both entities. In your case, you want to connect entities that participated in the same meeting. To do so, you'll choose a field that contains a unique ID for each meeting: the OBJECTID field. By choosing a unique identifier field, you'll connect both participants of the meeting with an ID of 1, both participants of the meeting with an ID of 2, and so on.

12. For Choose key type, choose Entities. For Source entity key field, choose OBJECTID, and for Target entity key field, choose OBJECTID.

    

13. Click OK.

    The link chart updates with the links. There are 1,315 links in total, which is the same number as the number of meetings in the Meeting_Centroids layer. You may not be able to see the links by default.

14. Zoom and pan the link chart until you can see the links.

    

    Because there are so many, you may not be able to see them all even when zoomed far out. Some nodes have more than one link; that means they met with different people at different times. Most of the links connect a large number of people in the center of the link chart. There are also smaller networks of individuals who met under and to the upper right of the main network in the middle.

    Now you're ready to focus your investigation on your suspect, Person-494. You'll select the nodes that include this POI as one of the participants.

15. On the ribbon, on the Link Chart tab, in the Selection group, click Select By and choose Select By Attributes.

    

    The Select By Attributes window appears. This tool selects features based on an expression you create using attributes from the table.

16. For Input Rows, choose Meeting_Centroids.

    

17. In the expression box, create the expression Where participant_1 is equal to Person-494.

    Tip:

    Because there is a long list of POIs, it's recommended that you type Person-494.

    

    You also want to select meetings in which Person-494 was listed as the second participant, so you'll add a clause.

18. Click Add Clause. Create the expression Or participant_2 is equal to Person-494.

    

    Note:

    It's important to choose Or instead of And for the second expression. If you choose Or, any meeting in which Person-494 was either participant will be chosen; if you choose And, only meetings in which Person-494 was both participants will be chosen.

19. Click OK.

    The selection is made. The nodes of Person-494, as well as the nodes they are linked to, are selected. Because you based the links on meetings, this means that all of the people Person-494 met with are selected. There are eight selected nodes in total.

    Tip:

    If you can't see the selected nodes, zoom in.

    

20. On the link chart, point to each of the selected nodes to see which POI they correspond to and all the people they met with.

    

    Of the eight selected nodes, two correspond to Person-494: one in which they were listed as participant 1, and one in which they were listed as participant 2. Additionally, of the other six selected nodes, two correspond to Person-247 and two correspond to Person-2305. The remaining selected nodes are Person-411 and Person-1566.

Compare the link chart and the map

You've identified four individuals who met with your suspect. Next, you'll compare the selection in the link chart to the map. You'll dock the link chart next to the map so that you can view both simultaneously.

1. Drag the Link Chart view tab to the docking station to the right of the center.

   

   The link chart is docked next to the map. The meeting centroids that you selected in the link chart are also selected on the map. You'll navigate to the selected features on the map to see them better.

2. Under the map view, click the Zoom to selected features button.

   

   The map zooms to a single meeting area. Your suspect met with their associates at the same place.

3. If necessary, pan and zoom both the map and the link chart to see the meeting area and the selected nodes.

   

   You have a good idea of who Person-494's associates are. Next, you'll find out who their associates have been meeting with. In this way, you'll build out the network of associates to the next level of individuals, ones to whom Person-494 is connected by one degree of separation.

4. If necessary, click the Link Chart view tab to make it the active view. On the ribbon, on the Diagram tab, in the Selection group, click Select Connected.

   Note:

   If the link chart is not the active view, the Select Connected button will not be available.

   

   The selection is expanded to the next level of associates on the link chart.

   

   You now have a larger and more robust network of associates to potentially investigate.

5. On the Quick Access Toolbar, click the Save button.

   

   The project is saved.

   Tip:

   Alternatively, you can save the project with the keyboard shortcut Ctrl+S.

In this tutorial, you analyzed cell phone data in ArcGIS AllSource to find associates who met with a suspected criminal. In the real world, meeting location analysis can be performed on a variety of time-enabled track datasets, not just cell phone records.

The dataset you downloaded at the beginning of the tutorial includes the Vehicle_Data_All_Vehicle_Data feature class (contained in the Vehicle_Data feature dataset). It contains vehicle locations over time. For an optional challenge, try performing the meeting location analysis workflow on this feature class.

This workflow, in conjunction with other intelligence workflows that can be done in ArcGIS AllSource, can be used by law enforcement and military personnel to help track down offenders or insurgents.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• The Camp Lemonnier Intelligence Data is fictitious sample data created for this tutorial by David Attaway, PhD, Esri.
• World Hillshade map: Esri, Airbus DS, USGS, NGA, NASA, CGIAR, N Robinson, NCEAS, NLS, OS, NMA, Geodatastyrelsen, Rijkswaterstaat, GSA, Geoland, FEMA, Intermap, and the GIS user community
• World Topographic map: Esri, HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors, and the GIS User Community