Working with Spatial Data Part IV – Developing a Geospatial Dashboard (GIS)

This article leverages the examples and concepts explained in the third part of this spatial data series which develops a “BI-Satellite” app ( Reporting spatial data with SSRS 2008 R2 and Reports Builder 3.0 Part-III )

Overview:
In the previous part of this article series, we saw how we can report spatial data on a map, where we used a dataset of modest complexity. But in practical business applications, data is much complex and detailed in nature to facilitate analysis. Typically, large volumes of data are then aggregated and presented in a analyzable form for Business Performance Measurement (BPM) on a Dashboard. Generally, a dashboard is formed from some common components such as Scorecards and Reports. In my view, for geospatial data analysis, a geospatial report can act as a mini dashboard in itself and so can be a “Geospatial Dashboard” (GIS).

Continuing from Part III, we have an SSRS Report which displays geocoded data on a geographical map using Bing Maps layer. The objective of this article is to discuss and develop a more advanced analytical report using the data we already have in the “Address” table. A geospatial dashboard should have categorized information, indicators deriving values intelligently based on comparison of actual performance vs targeted performance, and some actionable items for each data point represented on the report. To realize this concept, we need to implement the below high-level steps:

1) Modify the existing dataset and make it more detailed to facilitate a broader level of analysis
2) Make the report more analytical so it can reflect performance in an analytical form.
3) Associate actionable items with the data points

Implementation:

1) Firstly we need to make our “Address” table more detailed with some performance data as well as data that can be used as an actionable item. We have data that is related to some of the world’s most famous buildings, so number of visitors ( in units of million ) would be a logical piece of data to consider for performance measurement. If the number of visitors is low for any location, we would like to view the details of the place to examine the possible reasons for the this. For this we can add a wiki url which the user can open from the report, as an actionable item. From a categorization point of view, we need to categorize this data into a few groups, and continents seems to be a logical category.

2) Modify the address table and add three new fields named “Continent” (varchar datatype), “Visitors” (int datatype) and “Wiki” (varchar datatype). Populate these with relevant information as shown in the below screenshot.



3) The report which we developed in Part-III looks like the below screenshot. This report cannot answer two questions:

a) How to identify any data point into any category, as all the locations are represented be a red-coloured star
b) How to identify the performance of these locations (i.e. number of visitors in each location) within respective groups or across the entire dataset



4) Open this report in Report Builder, and edit the BingMapsDS dataset. Click on refresh fields and it should include all the new fields we added to the Address table.

5) Click on the map control, which will bring up a Map layers pane on the right side. Right click on the point layer and select Layer Wizard as shown in the below screenshot.



6) The New Map Layer page should pop-up on the wizard. Select “Analytical Marker Map” as the visualization type and move to the next page. We will go with the existing dataset “BingMapsDS”, so select this and move to the next page of the wizard.

7) This page of the wizard is the most important from a presentation viewpoint. The three main options are marker types, marker size and marker colors.

Marker types: This can be used to create a category or group among the entire set of datapoints displayed on the map. In our case, we have defined the continent field for this, so check “Use marker types to visualize data” and select Continent field. The effect will be that different types of shapes will be used to represent each group instead of just a single shape and this will be represented in legends tab. As of now, we just have a “star” marker which represents all the data points on the map.

Marker size: This can be used to display the weight or proportion of the data point in the dataset. The effect of selecting this is that the size of the marker type graphic will be based on the value of data selected for this field. We intend to use colours to represent the performance, so in our case using this option would overlap with this functionality. So do not select this for now.

Marker color: This can be used to show the performance of a data point compared to other data points in the dataset. In terms of a dashboard, a red indicator represents risk or poor performance and green indicators represent expected or good performance. So check the option of “Use Marker Colors to visualize data”, select the data field which we have included to measure performance i.e. Visitors, and we will use a color scheme similar to the one used in dashboards i.e. “Red-Yellow-Green”.

After this selection, your screen selection should look similar to the below screenshot.



Continues…

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