Introduction

The basic presentation unit for independent projects from OmniSci is the dashboard. OmniSci has created analytical visualizations of the data that fall under three categories:

• Geographic maps
• Statistical Graphs
• Presentation of Numerical or textual parameters

Linked tools

All of the tools in the dashboard also to constrain the data that is represented. You can manually select a window of the data directly on the graphical tool. All the maps that are created within a dashboard are linked together: Constrains on the data represented in one graphical tool will be constrained in all the others that appear in the dashboard. Map graphical tools allow for data geofencing by providing several types of tools to select an area on the map.

Measures and dimensions

• Dimensions: Dimensions are the grouped columns in a query. For example, if a table lists every car sold in the United States, and you want to display the number of cars sold per manufacturer, the dimension is car manufacturer (that is, the query results are grouped by manufacturer). A query can have more than one dimension. For example, the number of cars sold by car manufacturer, by state. When a chart has multiple dimensions, the dimensions are separated by forward slashes (/). For example, the car manufacturer by state would display “Chevrolet / California.” Table Charts display multiple dimensions in separate table columns. You can change the order of dimensions by dragging and dropping the dimensions. Certain chart types have restrictions on the types of dimensions you can use. For example, histograms and line charts can only have numerical dimensions. Choropleths can only have dimensions that reflect geographic regions. Pointmaps have no dimensions, since they display geographic points at the latitude/longitude level only. Number charts have no dimensions, since their purpose is to present only a single number.

• Measures: Typically, measures are calculated fields such as SUM, AVERAGE, and so on. Immerse creates charts by plotting these calculated values. This lets you visually compare measures of one dimension with another. If you select a numerical column as a measure, you can aggregate that column as AVERAGE, MIN, MAX, SUM, #UNIQUE, STDDEV, or SAMPLE. If you select a string column (that is, a column of text values), Immerse automatically aggregates by #UNIQUE, which returns the count of distinct strings in that column. Sample is different from other aggregate choices. Ideally, you use sample when there is one and only one value in the measure column that corresponds with the selected dimension. In that scenario, sample greatly improves the performance of your query. If there are multiple measure values, the results are likely to vary each time the chart redraws. Depending on the chart, Immerse can visualize anywhere from one to four measures. A Pie chart, for example, offers 2 measures, Size and Color. Whichever column you choose as the size measure is used to determine the size of the slices in the pie chart. Color is an optional second measure for Pie Chart, allowing another layer of information to be visualized on the same chart. For example, you can size the Pie’s slices by the number of sales opportunities that are closed, and color them by the average income from those sales.

SQL operations

In addition to normal measures, which perform simple aggregation calculations on data, you can create custom measures that perform arbitrary aggregations for any OmniSci supported SQL.

To create a Custom Measure:

1. Create or edit a chart.
2. From the Add Measure pop-up, choose Custom SQL Measure.
3. Enter an Alias for the custom measure.
4. Enter the SQL aggregation clause in the Custom Measure field.
5. Click OK.

Graph Filters

You can create several graph filters directly on the user interface by selecting part of the data. Then a filter icon appears on the graph. Clicking on it eliminates the filter.

Tools that we can use in Downtown

• Point Map: This allows to create the heat map for the clients.
• Bar Map: This will be useful to control filters like zip codes or demographic data based on number of records
• Line Map: This allows to define a timeline filter control of the data.
• Filtered pie chart: we can use this in combination with geofencing to define absolute variable numbers in an area of the map with respect to things like demographic strata, etc.