Intensity Chart (Heatmap)
Complex yet powerful analytics tool
Last updated
Complex yet powerful analytics tool
Last updated
Intensity widget is complex analytical widget mostly being used to create heat maps in time domain.
Users can analyze intensity of any system in time domain with different data parsing options.
It can be used to understand patterns, changes, spikes and other time dependent changes in the data.
It mostly being used to understand energy consumption patterns, resource and water usages, asset usage analysis, bottleneck analysis, inefficiency analysis, system loads, heat maps and their change in time.
It is color codded and X and Y axis is dependent on time intervals which can be configured according to data parsing configuration.
Time frame can be selected according to user need and can be last 24 hour, last 7 days, last 30 days, last 180 days or custom time frame.
Due to inconsistent data flow from IoT sources by their nature and pixel based structure of intensity chart, data being parsed with pre-determined time intervals and parsing algorithms and can not directly connect to data flow itself without parsing.
Data can be analyzed with 4 different time interval option which are hourly, daily, weekly and monthly.(Interaction Period)
Data parsing configuration also provides 3 algorithm option to parse the data for every data time interval option selected.
These are:
-Average: Takes arithmetic average of all data falls in selected time interval.
-Percentage: Rational difference between first data and last data in selected time lapse.It is used to understand change as ratio in time lapse. Can be used for consumption or counting analysis.
-Differential: Nominal difference between first and last data in selected time lapse. Can be used to understand extreme changes.
After selecting time frame, interaction period (time interval) and data parsing algorithm, users can create a color gradient with color palette for minimum and maximum threshold values and notes to depending on values.
Users can also create mid values in order to create more detailed color pallet and notes.
For direct visualization of time dependent data, please use .
