analyse data intuitively

All your information at your fingertips. Real-time and historic. Making cross sections at will. Exploring trends and black spots. Thus gaining a proper understanding of what is happening in the park, where, when, how often, who is being involved. And most important: providing you with actionable insights.

That’s the ambition and promise of Focus 360 Analytics: browse through all your data sources, as if they are one, and find patterns among incidents, poached species, kind of snares being used, and other issues of relevance.

How achieve this by automatically transforming your data into meaningful concepts and enabling you to simply select and visualise them on maps, timelines, and graphs.

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In one or two clicks I have a summary of what I want in relation to any incident. That is really awesome! 

Moses L., Lead Analyst, Kenya


TRANSFORMING DATA INTO CONCEPTS

Where data is just data, concepts represent actual things in the world.

Concepts, automatically recognised and used to create summaries and selections

For example, bike and bicycle are both data. Both are labels of the same concept: a vehicle with usually two wheels and pedals, a steering wheel and a saddle. In common systems, when you search for a bike, you will not find bicycles. Focus, however, understands you are looking for the concept bike, thus will return all records that contain bicycles as well – even if you misspel the word or or when nicknames or other languages are being used.

Likewise, you can search for a vehicle, and find bikes, scooters, trucks, Mercedes, and BMWs. While if you search for a motorbike, you will only find motorbikes, including Harley Davidsons, Suzuki’s and other motorbike brands. Including all its variations in spellings, nicknames and even misspellings. Hence, very much like you would search and find things yourself.

The result:

  • More intuitive and meaningful search and complete search results

  • More powerful and easier analytics, and

  • Automated similarity suggestions. Example: the modus operandi of this incident looks very much like the one used in these and these incidents.

The major concepts included in Focus are especially being designed and categorised for nature conservation and wildlife protection purposes. They include:

  • Findings, including offences, recoveries, and signs such as footprints, tire-prints, gunshots, etc.

  • Natural resources, including many species of fauna and flora

  • Typical ranger responses, such as removing snares or following tracks.

  • Periods (days of the week and months of the year)

Using Focus, you can combine these concepts (Filters) with geo-spatial functions (map) and time-spatial functions (time-slider) to make any type of selection.

By default, all available data sources and concepts are included in your analysis. However, by selecting one particular data source or concept, you can restrict your analysis to that particular data source or concept only. Likewise, by excluding one data source or concept, you can analyse all data sources or concepts except the excluded one.

TYPES OF DATA SOURCES THAT CAN BE INCLUDED

Focus can handle all sorts of data, including:

  • Cluey Tracker data

  • custom databases, spreadsheets, and documents

  • custom maps and map layers

  • third party systems adhering to open standards

  • trackers and tracers

  • sensor data,

  • photos and

  • web feeds

Likewise, data inside the platform can be exported and shared with third party systems, given they adhere to open standards.

SEARCHING FOR MEANINGFUL RELATIONS

The idea of graphs is that you can find meaningful relations between observations. For example, because they all share the same type of species, offences, arrestees, vehicles, snares or tools.

In a Focus graph, you can see clusters of incidents and can get specific information related to an incident, like things recovered and photos made at the scene. In the legend (right lower corner) you can select or exclude concepts that are not relevant for your search.

Below you see the relations among incidents recorded in the last month. Two people have been officially warned for charcoal burning, while one person has been arrested for poaching. He was found with the carcass of a dikdik and a panga (large knife).

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ENRICHING YOUR DATA WITH ONLINE KNOWLEDGE

As world community, we know much more about for example a Kalashnikov or an Eland then the average person. By linking the concepts (see above) of Kalashnikov and Eland to an online resource such as Wikipedia, all characteristics of these concepts become available to the analyst, or commander in charge for that matter (implemented).

The next function we are working on: making available all characteristics of the concept for inclusion in your analytical questions. For example, without having any information about it in your own database, you will be able to make a list of all incidents in which endangered species have been involved.

Connecting own data with Linked Open Data, in this case about Kalasnikov

Connecting own data with Linked Open Data, in this case about Kalasnikov

Connecting own data with Linked Open Data, in this case about Eland

Connecting own data with Linked Open Data, in this case about Eland

TRACK LENGTH / RANGER-TEAM / DAY

The overview below is made in 3 clicks:

  • use Data Sources to select Tracks (containing all tracker data)

  • use the time slider to select 1-11-2018 to 30-11-2018

  • click on Timeline to see all tracks in that period, including their individual length (in km)

Meanwhile, in the Filter section you see the number of tracks recorded per team in November. You can further specify the selection of tracks in the Timeline by selecting any of the teams.

Track length per ranger team per day, ordered on Timeline

Track length per ranger team per day, ordered on Timeline

User-selection of data ordered on Timeline.

User-selection of data ordered on Timeline.