SpatialKey focuses on bringing the power of location intelligence to decision makers – without all the hassle. The September release is focused on taking our powerful features and making them even easier to use.
An overview of what you will find in this release:
- New Report Control Bar
- More intuitive visualization and filtering pods
- Revised Map Layer manager
- New Map control manager
- Simple yet powerful proximity filtering
- New geoset filtering
We’ve made it easier to work with your datasets within reports. The Report Control Bar lets you do two things—add new datasets with the Add Data Panel, and manage existing datasets with the Dataset Configuration Panel.
Figure 1.1 Report Control Bar allows you to add new datasets and manage existing ones.
Users with lots of datasets will appreciate the new Add Dataset Panel (Figure 1.2), which makes it easy to sort through a large listing of datasets. Geosets (datasets containing shapes) are in their own section, where you can select an existing geoset or create a new one containing authored shapes. We’ve also exposed the custom folders that you can create in the Home Interface.
Figure 1.2. The Add Dataset Panel is used to add datasets or geosets to a report.
When you add a dataset to a report, a corresponding item is added to the Report Control Bar (Figure 1.1). When you click a dataset in the Report Control Bar the Dataset Configuration Panel opens. This panel provides all of the configuration options for the dataset.
Figure 1.3 The Dataset Configuration Panel
From within the Dataset Configuration Panel you can:
- Add a pod or filter with a wizard-driven interface
- Modify the color of the dataset and set a custom name
- View or remove filters that are applied to a dataset
- Enable proximity filtering to use this dataset as a filter on other datasets
- Export your filtered data to CSV
The visualization and filtering pods have been redesigned with several improvements:
- Pods use less space, allowing more pods to be arranged on the screen at one time.
- A pod’s color matches the color of the dataset, making it easy to quickly identify which dataset a pod is related to.
- Configuration options are organized into a panel (Figure 2.1) that provided customized options for each type of pod.
- It’s easier and more intuitive to switch the numeric field that is visualized (Figure 2.1).
Figure 2.1 Pod Configuration Panel
Figure 2.2 (below) uses a numeric histogram to show the number of real estate sales based on the number of bathrooms in a home. By clicking the Count >> button within the pod you expose the configuration panel, which allows you to quickly switch between any of the numeric fields in your datasets. In Figure 2.3 you have switched to showing the average price vs the number of bathrooms.
|Figure 2.2||Figure 2.3|
New, customer-requested features include the ability to show or hide the Manager, see which numeric column from your data is being visualized on the map, and easily switch between the data being visualized on the map and an enhanced dataset Settings panel.
Figure 3.1 Revised Map Layer Manager
Show or Hide the Manager / Scroll Datasets
To conserve space on your maps, hide the Map Layer Manager by clicking the pin icon (see Figure 3.2). To expose the Manager again, move your mouse to the bottom of the map. To disable auto-hide, simply click the pin icon.
Note that if you have several datasets in a map, the scroll buttons become enabled (see Figure 3.3). This allows you to scroll between datasets.
Figure 3.2 Map Layer Manager pin
Figure 3.3 Map Layer Manager scroll
The new Map Control Manager adds some customer-requested features and makes these controls less obtrusive. The new pod is streamlined and takes up less room on the map (see Figure 4.1). Settings options slide out from the Map Control Manager for easy access. The pod itself is now divided up into four sections each corresponding to function (including some new features):
|Figure 4.2 Map Zoom||Figure 4.4 Map Filter|
Our Proximity Filtering feature allows you to search for records in one dataset based on their proximity to records in another dataset. In this release, we have re-designed this workflow to make this powerful feature easy to use. The best way to explain the use of this feature is with an example:
Crimes near bus and train stations example
Suppose we have a dataset containing crime information and a geoset (a set of shapes) containing the locations (and boundaries) of bus and train stations (see Figure 5.1). If we want to find out how many crimes occurred within one mile of those stations we can use Proximity Filtering. Proximity filtering is enabled by selecting the Proximity Filter tab in the Dataset Configuration panel (see Figure 5.2).
|Figure 5.1 Unfiltered crime and bus/train stations||Figure 5.2 Proximity Filtering Menu|
Here are the steps to enable Proximity Filtering for this example:
- Click the Bus and Train Station dataset within the Report Control Bar to open its Dataset Configuration panel.
- Select the Proximity Filter tab.
- Set the proximity options.
- Make sure “Include only” is selected.
- Set the distance (1 mi.).
- Check the appropriate check boxes for which datasets to apply the filter to – in this case, the Sacramento Crime dataset.
- Click the “Enable” button.
Once enabled, the records will be filtered by the bus and train stations and the crime heatmap will update (see Figure 5.3).
Figure 5.3 Filtered crime near bus/train stations
Note that when a dataset is used for proximity filtering, it is “locked” which means you can’t add or modify filters in that dataset. This is done as a precaution because proximity filtering costly process, especially when many points and large polygons are involved. If additional filtering is required on the locked dataset, simply disable Proximity Filtering, make your changes, and then re-enable when complete.
Also note that proximity filtering can be applied from both point-based and shape-based datasets, or from shape-based geosets. For example, if you had a dataset of store locations and wanted to filter out a demographics dataset to find the average income within a 5 mile radius of your stores, you could use proximity filtering to accomplish this.
Once imported into SpatialKey, shapefiles containing shapes become geosets. (Shapefiles containing points become standard datasets.) In this release, you can now filter geosets based on attributes (”extended data” imported from shapefiles) just like you can with field data in datasets.
Take a look at Figure 6.1. Here we see a Unique Value List pod of all of the shapes in the Sacramento ZIP codes geoset. By double clicking on the Elk Grove element in the pod, you narrow the shapes down to only those three that are near that city (Figure 6.2). With this feature, you can easily narrow down a large number of shapes in the same ways that you can interact with a standard dataset.
If this filtered list of ZIP codes was then used as a Proximity Filter, you could search for records in another dataset that are within (or near, or not within) these three ZIP codes.
|Figure 6.1 Unfiltered geoset||Figure 6.2 Filtered geoset|