Our step-by-step guide to create a TSVB visualization in Kibana to observe how spaces are used over the course of a week.
Learn how we at reelyActive use Kibana TSVB to analyse and compare zone utilization.
reelyActive open source software with Elasticsearch and Kibana.
In order for there to be data to visualise, the reelyActive software must also have collected and written raddec data to Elasticsearch.
Create a TSVB in Kibana
Open Kibana and then:
The default settings will result in a time series visualization with unfiltred data. The next step will be to define a meaningful filters in order to reduce the unnecessary information that can confuse the analysis.
Define a meaningful set of metrics and filters to understand zone performance
Cardinality means the number of distinct values in a table column. By choosing the field transmitterId.keyword that will count the transmitters once and will reduce the number of counts effectively.
Each sensors has a fixe position and refers to a location, ex: room, zone, floor etc. Define which zone you want to compare by entering each location in a specific filter as below:
RSSI is used to approximate distance between the device and the sensor. At maximum Broadcasting Power the RSSI ranges from -35 (a few inches) to -100 (40-50 m distance). By applying RSSI filters you can display only tags within a certain distance.
To measure anonymously the occupation of spaces, our sensors detect Bluetooth devices that people already carry on them (smartphones). Bluetooth devices such as smartphone use a 48-bit random device address which is classified as 3. This filter removes most devices that are not associated with people like smart TV.
In most cases, devices are decoded multiple times. A decode filter is used to suppress all noise signals decoded only a few times.
Analyse data and identife trends and patterns
By default the captured data are presented in numbers and do not reflect the exact number of people present in the spaces for many reasons:
To protect mobile devices from being tracked as they move there is a technique known as MAC address randomization. This replaces the number that uniquely identifies a device's wireless hardware with randomly generated values.
Moreover, the captured data come from devices that are mainly worn by people. Most likely, some people have more than one device for work and personal reasons.
This is why the data sensed by the reelyactive sensors does not constitute a people counter but reflects the occupancy trends.
Visualize the data in percentage as below:
To reduce the percentage to one hundred, it is possible to divide the data by a ratio.
divide(params.value,ratio)where the ratio is a determinated number
This visualisation can be combined with other visualisations as part of a space occupancy dashboard, such as that below.
Create other visualizations, or continue exploring our open architecture and all its applications.