The project contains a map with point data in the San Francisco Bay Area. The data was collected via the Gowalla social media platform, which was active between 2007 and 2012. Gowalla allowed users to check in at locations they visited. Each point represents a location where a Gowalla user checked in.

Based on the map, answer the following questions:

• Do certain places contain more check-ins than others?
• How might you define an area to be popular using these check-ins?
• The data is densely clustered. How much insight can you gain just from looking at the map?

Next, you'll investigate the data's attributes.

The table appears.

The User ID and Location ID fields contain unique IDs for users and locations. You don't have access to a key for these IDs, so these fields aren't useful to determine popularity. The Check-in Latitude and Check-in Longitude fields provide the data's spatial information, while the Check-in Time field provides its temporal information.

The Geoprocessing pane appears.

The output layer named Bay Area Gowalla Check-ins is added to the map.

The layer is removed. Although you've projected the layer, the map's appearance hasn't changed. The map is still using the original projected coordinate system, which is focused on the United States as a whole (meaning California, on the edge of the United States, is somewhat distorted). You'll update the map's projection.

The Map Properties window appears.

The map changes to use the selected coordinate system.

This tool creates a grid of regular polygon features, such as hexagons, squares, or triangles, to cover a specified extent.

The tools runs and a hexagon grid is added to the map. (Its default symbology is random and may differ from the example image.)

Next, you'll count the number of check-ins within each hexagon bin. You're not interested in areas where no check-ins were made or where no data was collected, so first you'll select the bins that intersect at least one check-in.

When running a geoprocessing tool on a layer that has an active selection, such as your hexagon grid, the tool only uses the selected features for analysis. Features that aren't selected won't be used in the analysis.

The Select Layer By Location window appears.

• For Input Features, confirm Hexagon_Tessellation is selected.
• For Relationship, confirm Intersect is selected.
• For Selecting Features, choose Bay Area Gowalla Check-ins.

On the map, hexagon bins that intersect at least one check-in are selected.

Next, you'll join the check-in features to the selected hexagons. The join will add an attribute field to the hexagon grid that includes the number of check-ins within each hexagon.

• For Target Features, choose Hexagon_Tessellation.
• For Join Features, choose Bay Area Gowalla Check-ins.
• For Output Feature Class, type Check_in_Counts.

The tool runs and a new layer, containing only the selected hexagon bins, is added to the map. The check-in counts for each bin are contained in an attribute field for the layer. To visualize the counts on the map, you'll change the layer symbology.

The Symbology pane appears.

The symbology is applied to the map.

On the map, pink hexagon bins have a higher number of check-ins, while blue bins have a lower number. The bins with more check-ins tend to be grouped around San Francisco and San Jose, the largest cities in the area.

The tools runs, but no layer is added to the map. Instead, a report file was created. You can find the path to this report file by viewing information about the tool.

The Spatial Autocorrelation (Global Moran's I) window appears. This window lists the tool's run time, the parameters you used to run the tool, and any warning messages.

The report file appears on a new browser tab.

The report includes the Moran's Index, the z-score, and the p-value. For determining statistical significance, the z-score is the most important of these values.

The z-score indicates the number of standard deviations a value is from the average value. Positive z-scores are values above the average, while negative z-scores are values below the average. In this case, the value being measured is the amount of spatial autocorrelation that exists between features in your dataset.

Your data's z-score is over 7, meaning that your data has significantly more spatial autocorrelation when compared to a hypothetical collection of randomly distributed data. The report also contains a chart that plots the z-score on the far right end of a bell curve. The chart indicates that there is statistical significance to the distribution of your data, and that it is clustered (meaning similar values in the data are closer together).

This tool provides three methods for spatial clustering, with each method requiring a different definition of what is considered dense and not dense. You'll run the tool three times, one for each method, and weigh the advantages and disadvantages of each.

First, you'll use the defined distance method, also known as DBSCAN, which is the simplest density-based clustering method. In this method, density is defined by having a specified number of points within a specified distance. At every point, it checks whether the point satisfies the minimum number of features within a set search distance. If a point satisfies this criterion, it is marked as a clustered point. To run the tool, you must define the minimum number of features. You can also define the search distance, but if you do not set a search distance, the tool uses an optimized value.

The minimum number of features per cluster depends on your data and the problem you want to solve. You want to identify popular places in the Bay Area. You don't know an exact number of check-ins that makes a place popular, but you can define a number based on your business' circumstances. For instance, assume you want to open a night club in the Bay Area and plan to charge fees that will require at least 500 customers per day to make a profit. In this example, you can define the minimum number of features per cluster to be 500. You can set the search distance to about 0.1 miles, roughly the size of a city block.

• For Input Point Features, choose Bay Area Gowalla Check-ins.
• For Output Features, type DBSCAN_500.
• For Clustering Method, choose Defined distance (DBSCAN).
• For Minimum Features per Cluster, type 500.
• For Search Distance, type 0.1 and choose US Survey Miles.

The tool runs and the result layer is added to the map.

On the map, the colored points represent dense clusters of check-in points. The gray points represent noise, or any location that did not fit your definition of dense.

The legend provides insight on the symbology:

Density-based clustering can find hundreds of clusters in a dataset. Rather than symbolize each cluster with a different color, eight distinct colors are used. The results are displayed so that clusters with similar colors are not close together, making the distinctions between clusters clearer on the map. The colors do not correspond to any attribute in the data.

On the map, clusters are primarily located in San Francisco and the South Bay, with a few clusters located in other places. You'll change the basemap and zoom in to learn more.

San Francisco contains several clusters, including a particularly large blue cluster to the northeast. This cluster is downtown San Francisco.

Berkeley contains a single cluster, located in the center of the city.

Palo Alto and the surrounding area contain a few clusters. The Stanford Shopping Center (orange) and downtown Palo Alto (pink) are detected as clusters.

San Jose is the most populous city in the Bay Area, even more than San Francisco. However, it contains fewer clusters than San Francisco.

The map extent returns to show the entire Bay Area.

Overall, there are only a few clusters outside of San Francisco. One of the limitations of the DBSCAN method of clustering is that it uses a fixed distance for determining density. (When you ran the tool, you set this distance to 0.1 miles.) The distance chosen can significantly influence results. While a smaller distance may be appropriate for areas such as downtown San Francisco, where stores and other points of interest are close together, it may not be appropriate for suburban or rural areas where stores are more spread out.

Your study area encompasses cities, suburbs, and rural areas, so using a single fixed distance may not provide the best results. Next, you'll perform density-based clustering using the self-adjusting method, also called HDBSCAN.

HDBSCAN detects clusters at multiple search distances, similar to if DBSCAN were run multiple times. At each search distance, it detects different clusters in different locations. Then, DBSCAN attempts to merge these clusters to create larger clusters that all have a similar point density. The resulting clusters are not defined by a single search distance.

The tool no longer requires a search distance.

Compared to the DBSCAN method, the HDBSCAN method detects more clusters. Clusters appear all over the Bay Area, including in rural areas, and some of these clusters are large enough to cover entire cities, such as the clusters in Santa Rosa or Vallejo. While these results indicate more popular locations in the Bay Area, the results may not be enough to pinpoint the best place to open a new business.

Next, you'll use the third spatial clustering method, multi-scale (also called OPTICS).

The OPTICS method records the distance between the first feature in a dataset (Order ID 0) and its nearest neighbor. This distance is called the reachability distance. Then, the method records the reachability distance between the nearest neighbor and its nearest neighbor. This process repeats continually until the entire dataset has been covered. No nearest neighbor is repeated; if one feature's nearest neighbor was also the nearest neighbor of a previous feature, the next nearest neighbor is used instead.

Then, the OPTICS method charts all of the reachability distances and looks for peaks and valleys in the chart. A valley, or a group of features with relatively low reachability distances, is a cluster of points that are close together. Once all of the points in a cluster are charted, the next point, which is not part of the cluster, will have a relatively high reachability distance, corresponding to a peak in the chart.

The following diagram shows an example reachability chart and the corresponding clusters of points:

In this example, all of the blue points are close together, so there is a low reachability distance between them. (The red lines represent the reachability distance from point to point.) On the chart, these points correspond to the blue valley. Then, there is a relatively large distance between the last blue point and its next unique nearest neighbor, corresponding to a sharp increase in reachability distance on the chart.

In the green valley, there is a peak that is relatively small compared to the two larger peaks on either side of the valley. Depending on the OPTICS algorithm's cluster sensitivity, this small peak may divide the valley in two or it may still be considered part of the valley.

This method requires a search distance. By default, the search distance is set to the previous distance you used, 0.1 miles. This method also has an optional parameter, Cluster Sensitivity. You'll learn more about this parameter later. For now, you'll leave it blank.

The results of this clustering method are similar to the results of the DBSCAN method. The OPTICS method is similar to the DBSCAN method, but the OPTICS method accounts for clusters of varying densities by relying on relative peaks and valleys rather than absolute distances.

What the method considers a peak and a valley relies on its cluster sensitivity. You didn't set a cluster sensitivity, so the tool uses a sensitivity value based on the statistical spread of the data. You'll view the tool details to see what sensitivity was used.

The Density-based Clustering window appears with information about the cluster sensitivity value used.

The tool used a cluster sensitivity of 28. (The sensitivity value is always an integer between 0 and 100.) You'll run the tool again with different cluster sensitivities and see how the results change.

At this sensitivity, the clusters are relatively large.

The OPTICS_500_Sensitivity_0 layer, with a higher sensitivity, resulted in smaller, more compact clusters.

For your problem, locating a popular place where you can open a business, using a higher sensitivity is probably more useful. While a lower sensitivity may help you delineate broader areas of popularity, higher sensitivities indicate places where high numbers of check-ins are occurring—in other words, where people are actually going.

This tool converts time and date values from a text string to a date field.

Next, you'll set the input time format (the format that the field currently uses). The format is written using letters to represent different units of time, such as y for year and H for hour. The format used in the table is yyyy-MM-ddTHH:mm:ssZ, with the T and the Z being constants that do not reflect any units of time.

You'll leave the other parameters unchanged.

The tool runs.

The Check_in_Time_Converted field has been added to the end of the table with the converted check-in times.

The Bay Area Gowalla Check-ins - Data Clock 1 view and the Chart Properties pane appear. To create the chart, you'll change parameters in the pane. You'll create a chart that visualizes the total number of check-ins by year and month.

The data clock is created.

In this data clock, each concentric circle (ring) represents a year, while each circle segment (wedge) represents a month. The color of each wedge represents the total number of check-ins made during that month, with darker blue colors corresponding to more check-ins. Gray wedges have no data.

Your data clock has two rings: 2009 and 2010. Check-in data was first collected in March 2009 and last collected in October 2010. There were a low number of check-ins until late 2009 as the Gowalla service adopted more users. The months with the highest number of check-ins were March, April, August, and September 2010.

The data clock updates.

The data clock contains significantly more rings, but only seven wedges in each ring, one for each day of the week. Based on this data clock, the weekend days (Saturday and Sunday) have the highest number of check-ins. This pattern makes sense, as most people don't have to work on the weekends and thus have more leisure time to visit places.

Depending on the type of business you plan to start, you may also be interested in the times of day that check-ins occurred. Visualizing a year's worth of hourly data would be difficult, so you'll create a feature class that only contains a subset of the data and create a chart for it.

All check-ins made during the selected dates are also selected on the map.

In ArcGIS Pro, any geoprocessing tools run on a dataset will only be run on the selected features, if a selection has been made. Next, you'll copy the selected features to a new dataset.

The copied feature class is added to the map.

A new data clock is created.

The data clock automatically updates with 24 wedges, one for each hour of the day.

Few people checked in during early business hours, with especially low counts in the hours between 6 a.m. and 2 p.m. The highest volume of check-ins occurred between 7 p.m. and 9 p.m. and between 1 a.m. and 2 a.m. These trends may be indicative of a high influx of customers to restaurants during the evening or night clubs late at night.

For your subsequent analysis, you'll only work with the check-in data between December 2009 and September 2010, the 10 months when check-ins were at their highest. Using this subset of the data in subsequent analysis will remove records from when the social media app was still gaining users. These periods of low use may skew results.

The search returns three results for Create Space Time Cube.

The tool that you choose depends on your data. Your check-in data comes from a variety of point locations across space, so you want to aggregate points. If your data instead relied on stations or other locations with fixed geographies (such as traffic cameras or toll booths), you would create a space time cube from defined locations. If your data came from a multidimensional raster layer, you would choose the appropriate tool.

After you type the output name, the .nc extension is automatically added to the end. This extension stands for netCDF, which is the file type used by space time cubes.

Next, you'll choose the interval of time to aggregate points, or the time bin. The time bin interval should be appropriate for the time scale relevant to your analysis. You want to know if there have been any long-term trends in neighborhood popularity, so an hourly or daily bin would not be useful. Instead, you'll use a monthly interval. (If you planned to open a business that saw increased activity during specific hours of the day, such as a coffee shop, you might be more interested in an hourly bin to see which places are more popular during those times.)

You'll also choose the shape of the area for spatial aggregation. You'll use a hexagonal aggregation area, because hexagons have the highest number of spatial neighbors (6) of the available shapes. Additionally, in a hexagon grid, all neighboring hexagons are a constant distance away. Later, you'll define spatiotemporal neighborhoods by distance, so hexagons will have an advantage over a fishnet (square) grid, where some neighbors are farther away than others.

You'll set these hexagons to be 1 mile wide.

The tool runs and creates a space time cube file. No outputs are added to the map. To visualize the space time cube, you'll run another tool.

This tool creates a 2D layer based on an .nc file.

• For Cube Variable, choose COUNT.
• For Display Theme, choose Trends.
• Check Enable Time Series Pop-ups.
• For Output Features, type Check_ins_STC_2D.

These parameters will map the trends in monthly check-in counts. By enabling time series pop-ups, you can view a time series for each bin showing counts over time.

The tool runs and the layer is added to the map.

The pop-up contains a time series chart showing the number of check-ins over time at that location. Although there may be some decreases over time, generally there is a strong increasing trend in the purple bins.

The numbers on the vertical axis of the time series chart indicate the number of check-ins. The hexagon in the example image has gone from about 160 check-ins per month to about 360.

Green hexagons are those where a downward trend was detected. Many of these hexagons have low counts of check-ins overall. In the example image, the area decreased from a high of over 900 check-ins to a low of under 600. Even though the trend is decreasing, even the lowest values for this area are higher than the highest values for the area where the trend was increasing.

White hexagons are areas where no trend was detected, either up or down. These hexagons may have either stable numbers of check-ins per month or highly erratic numbers.

When you analyzed the data spatially, you found that downtown San Francisco was the most popular area. However, much of downtown San Francisco shows no upward or downward trend in popularity. On the other hand, areas in San Jose or the East Bay are growing in popularity. It may be worthwhile to consider these areas as places to open your business.

Next, you'll visualize the space time cube in 3D, which will make it easier to see changes in time on the map. (Time is the third dimension in a space time cube.) First, you'll insert a new scene.

A scene view is added to the project.

• For Input Space Time Cube, browse to the Check_ins_STC.nc file.
• For Cube Variable, choose COUNT.
• For Display Theme, choose Value.
• For Output Features, type Check_ins_STC_3D.

The tool runs and the result layer is added to the scene.

In this visualization, each hexagon bin has a height composed of segments, with each segment corresponding to a different month. The color of each segment indicates the number of check-ins in that area during that month.

Unlike the 2D visualization, each segment is symbolized by total count of check-ins, not by increasing or decreasing trends. As you saw in your spatial analysis, downtown San Francisco has the highest count of check-ins, even if it's not an area that is gaining popularity. Most bins in other locations have few check-ins and are symbolized white.

You return to the Map view.

You can also cluster the data by one of three characteristics of interest. You'll learn about the other characteristics later, but for now, you'll cluster so that locations with similar values across time are clustered together.

You can also set the number of clusters that the tool creates. If left unchanged, the tool will use an optimal number based on the data. You'll create three clusters, corresponding to groups of high, medium, and low popularity.

You'll also create an output table so you can chart the results.

The clusters layer appears in the map.

The hexagon bins are clustered into three groups: blue, red, and green. To find out what these clusters mean, you'll open the chart you created with the tool.

The chart appears.

In the Average Time Series per Cluster chart shown above, the blue hexagons are locations that historically have few check-ins. (They all have had at least one check-in, or they wouldn't be included at all.) The green hexagons are locations with more check-ins however, although the check-in counts are high, the number of check-ins fluctuates significantly from month to month. On the map, only one green hexagon was identified (in downtown San Francisco). These fluctuations may be due to seasonal variations in tourism. The red cluster contains downtown locations that may be frequented by locals, resulting in relatively consistent popularity throughout the year.

The pop-up shows the time series chart at that location. The dotted green line shows the average number of check-ins for hexagons in the green cluster.

You identified clusters of locations with similar numbers of check-ins over time. You can also identify clusters of areas with similar temporal trends. For instance, say that two areas encounter similar increases and decreases in check-ins over time due to seasonal changes in tourism. However, one of these areas has a significantly higher total number of check-ins than the other. When clustering based on value, these areas are not clustered together. But when clustering based on profile, they are.

Clustering locations by profile is useful for businesses that intend to target a specific seasonal crowd. Profile clustering can be accomplished by one of two methods. You'll use the Fourier Family-based time series clustering method. The Fourier method identifies areas with different changes to popularity throughout the year.

You can ignore certain characteristics of your time series when running the tool. You'll ignore the Range characteristic (in this case, the count of check-ins). This way, you'll identify locations with similar popularity trends regardless of the absolute number of check-ins. You'll also allow the tool to determine the best number of clusters to create.

The clusters layer appears in the map.

There are many more hexagons of each color when using Profile (Fourier).

In this chart, red corresponds to hexagons with more check-ins, especially during the spring. Blue corresponds to hexagons with fewer check-ins throughout the year, Green corresponds to hexagons with increasing check-ins. Each cluster type can be found throughout the Bay Area, rather than being tied to areas that have more check-ins in general (such as downtown San Francisco).

• For Input Space Time Cube, browse to and choose Check_ins_STC.nc.
• For Analysis Variable, choose COUNT.
• For Output Features, type Check_ins_Emerging_Hot_Spots.
• For Neighborhood Distance, type 1 and choose Miles.

For each location, EHSA will examine every neighboring location within a mile to perform its analysis. You previously created a space time cube with a hexagon grid, which is ideal for neighborhood analysis because each hexagon is equidistant.

Hot spots are located in downtown San Francisco, as well as several smaller cities south of the bay, such as Palo Alto, Mountain View, and San Jose. Most of the hot spots in downtown San Francisco are persistent hot spots, meaning they have been hot spots consistently across time. The other areas are mostly either new hot spots, meaning they were hot spots only at the end of the time series, or sporadic hot spots, meaning they have been hot spots some times but not others.

Note that areas that were characterized with high and medium count clusters by time series clustering show up as consecutive hot spots. This implies the vicinity of these areas is higher than the average number of check-ins for the Bay Area for most of the time steps. In other words, these areas were more popular than the rest of the Bay Area for most of the time steps in the space time cube. Unlike San Francisco, these areas appear to be increasing in popularity over time.

You can also visualize the results in 3D.

The hot spots layer appears in the scene.

Now that you've run EHSA on your space time cube, you can visualize based on the results of the analysis.

• For Input Space Time Cube, browse to and choose Check_ins_STC.nc.
• For Cube Variable, choose COUNT.
• For Display Theme, choose Hot and cold spot results.
• For Output Features, type Check_ins_STC_Hot_Spots.

In areas considered new hot spots, only the most recent month (the uppermost hexagon bin on the column) is considered a hot spot. Sporadic hot spots alternate between being hot spots and not being hot spots. In downtown San Francisco, areas are hot spots during every month, making them persistent hot spots.

When you ran EHSA, you chose a neighborhood distance of 1 mile. Changing the neighborhood distance also changes your results.

• For Input Space Time Cube, browse to and choose Check_ins_STC.nc.
• For Analysis Variable, choose COUNT.
• For Output Features, type Check_ins_Emerging_Hot_Spots_5mi.
• For Neighborhood Distance, type 5 and choose US Survey Miles.

When a larger neighborhood size is used, larger areas are considered hot spots.

When you performed cluster analysis, the result layers included the Cluster ID attribute field. In this field, any feature with a value of -1 was not a cluster. You'll select all areas that were clusters.

All areas indicated as clusters are selected.

Next, you'll remove the clause you just executed and select locations that are a new, consecutive, or persistent hot spot.

The hot spots are selected.

Next, you'll select monthly time clusters that see an increase in traffic during a specific season. Depending on the kind of business you plan to open, areas with more traffic in different seasons may be ideal. For the purposes of this exercise, you'll select areas with more traffic during the summer.

In this layer, the temporal cluster that corresponds to high traffic patterns in the summer months is the green cluster, which has an ID of 3.

You've selected areas based on three criteria. Next, you'll create a layer that contains only hexagon bins that are selected in all three layers (meaning they adhere to all three criteria).You could adjust the criteria, add more criteria, or remove criteria, depending on the specific needs of your business. For the purposes of this exercise, three criteria is enough.

Messages appear below each input feature, explaining that these layers have active selections.

The ideal locations can be found in San Francisco, Mountain View, and San Jose.

Your analysis has identified some areas in San Francisco that would be ideal locations to open a business.

While many points in Mountain View have been identified, these points are all grouped around a single area: Mountain View's downtown. If you wanted an alternative to San Francisco (perhaps because costs are too high), this area would be ideal.