Once you have imported a dataset, with a point (location) column, you can use the visualization tool to create point, heat, and region maps.
Accessing the Visualization Tool
From the landing page of your dataset, simply click on the Visualize button and select Create Visualization. Or, from within the dataset, click the green Visualize tab and click on Launch New Visualization. This will open a new page where you can begin building your visualization!
To get started, you'll want to click on the + Add Visualization button in the upper right corner of the page.
Creating Your Map
Once you do this, you'll open up the visualization editor. To create a Map, click on the globe icon to start the map creation process!
Once you have selected the globe icon, some details will immediately generate. To the left of the page, there are two menu buttons that can be used to customize your map visualization: Map Layers and Map Settings.
The Map Layer section will be where you configure, shape, and stylize the data that is used to generate the map. You can also view and select other layers to add to your map, you can find more information on creating multilayer maps here.
The Map setting section will be where you configure the behavior of the map across layers. Here you can adjust things such as the title, description, base map, and search options.
We will first explore all the options available to customize your point map in the Map Layers section.
The main component of your map can be found under Geo Column. When selecting the globe icon, this field will pre-populate with the location column. If your dataset has more than one location column, you can select an alternative from the list of columns below the dimension field. By default, the map will start as a point map, but it can be changed to either a heat or region map in the Point Aggregation section.
Resize Point By Value
This section allows you to choose a number column whose values you can use to resize the points on the map.
This section will allow you to modify the point colors based on the values of any column in your dataset. You can also set an icon to be used for any value and color code that icon.
In this section you have three options:
- None - Keeps your map as a standard point map
- Heat Map - Creates a heat map
- Region Map - Creates a region map utilizing custom boundaries already created on your domain. Please note that region maps will not function correctly on datasets that have multiple location columns.
- Select Custom Boundary - A dropdown populated with custom boundaries set up in Spatial Lens.
- Measure - Define how your region map is measured, select either a count of rows in the dataset or measure any numerical column using Sum, Average, Median, Min, or Max.
In region maps, you can select the color palette as well as the number of data classes the region map should be broken into.
You also have the option to select one of two Classification Methods.
- Jenks - Existing default selection which you can read about here.
- Linear - Simple linear regression breaking data classes into even distribution buckets based on the data in the dataset. The lower bound is inclusive whereas the upper bound is exclusive, so 504.6–1004.2 means "504.6 up to but not including 1004.2." However, for the last bucket (i.e., the highest values), the lower and upper bounds are inclusive, so in the example below, 2003.4–2503 means "2003.4 up to and including 2503."
Standard Point Maps
Choose a color or color palette (when coloring by value) for the points on your map. You can also choose the opacity level of the points themselves.
You can also add an icon to your map point by using the Icon tab and performing a keyword search to find the best icon from the available icon library.
Choose between More Detail, Normal, and Less Detail. The level of detail can affect the performance of the map, with more complex data you may want to choose less detail in order to increase performance.
Choose the size of your point or the size range (when scaling points by value). When choosing the size range you set both the minimum and maximum size of a point to be used in the map. You can then select a number in the dropdown for "Number of Data Classes". Each data class will represent a single point size in which a subset of your data will fall into. If you select five data classes, the map will have five possible point sizes and your data will be placed into a corresponding bucket based on its value.
Flyout Unit Label
Edd the singular and plural version of the unit label used when hovering over a point.
When clicking on a specific point, these are the details that will be displayed. You can configure a column to be used as the Flyout Title as well as add additional columns to your flyout details.
A checkbox to show/hide the map legend.
In this next section, we can set a number of different map setting options.
Set the title and description of the map, this will show up in the map area but will not change the name the map is saved as. You can also toggle the "View Source Data" link on and off, this link will direct users back to the dataset used to create the map.
In the Type drop-down, choose from a list of nine prepopulated base maps, the map will refresh with your selection.
In this section, you can choose wheater you want to show three different controls: the search bar, the location button, and the navigation button. The search bar allows you to search for a specific location, the location button will zoom to the point where you currently view the map, and the navigation buttons will allow users to zoom in and out.
You can select a custom search boundary on the map by selecting shift + click to draw a box on the map that sets custom boundaries. These boundaries will bound your search results on the Map.
Point maps are a useful and intuitive way to show where incidents are occurring in a given region. They do, however, have their limitations - specifically, when looking at dense data, looking at points when zoomed out, and when multiple points occur in the same location.
In these cases, Socrata uses two different point aggregation features to make point data more useful and intuitive - stacking and clustering. Socrata’s geospatial visualization tools allow users to configure these capabilities to ensure that the final visualization is useful and meaningful.
What are Clusters?
Points grouped together based on geographic proximity, represented by a circle with a number inside. Clusters show a high-level view of your point data when zoomed out. Clicking on a cluster zooms the user into a closer view of the points contained in that cluster.
What are Stacks?
When points are very close together or located at the exact same coordinates, we render them as “stacks” (small circle with a colored outline) rather than have them all overlap. Clicking on a stack “spiderfies” out the points so that you can easily interact with each one.
Stop Clustering at Zoom Level
At this zoom level, show individual points instead of clusters. 1=zoomed out; 22=zoomed in. Note: If your map contains a lot of data, we may render clusters at lower zoom levels than you set (for performance reasons).
Adjust the size of the area by which points are grouped together into clusters. A smaller radius will produce more clusters, and a larger radius will produce fewer.
Make clusters look bigger or smaller. Note: If you have set a “Resize by Value” column, that data will dictate cluster size rather than this control.
Adjust the size of the area by which points are grouped together into stacks. A smaller radius will render more individual points.