UNEP-WCMC
(UNEP World Conservation Monitoring Centre)
2020 projects:
Potential contacts for 2020:
Mike Harfoot <Mike.Harfoot@unep-wcmc.org> (computational modelling)
Ben Tregenna <ben.tregenna@unep-wcmc.org> (data infrastructure)
Outlines for 2020
- Travelling Businesswoman Problem
We all need to reduce our Carbon Footprints and for many businesses, flights are a major component of this. Design an online system to help an SME (~100 staff) reduce its footprint by optimising planned travel on an annual basis. Staff should be able to input potential business trips to other institutions, conferences and meetings as well as contacts or locations that it would be beneficial to visit if the opportunity arose. The team should consider simple ways to collect this information and to display advice or suggestions during the planning of a trip.
- Support Sustainable Wildlife Trade
UNEP-WCMC supports many developing countries in assessing the potential levels for sustainable trade in wildlife for particular species. The challenge for this team will be to use the data provided by existing APIs to power a tool for countries to follow a methodology for producing these reports for a given species/family. The tool must be easy to use in target developing countries and allow the user to provide additional local knowledge to supplement the global data provided by existing data APIs and finally produce a summary report for future policy use.
- Ecosystem game/visualisation
UNEP-WCMC have been pioneering the development of an agent based model of whole ecosystems, the Madingley model. This process based formulation provides an engine that can be used in many ways. One of the key ways we would like to use the model is to engage with the public and/or decision makers. So, the challenge for the team working on this project will to consider options for improving the way the model can be used, either through a game interface or a visualisation interface for the model engine. The principal aim will be to make the model more engaging and exciting.
Previous years:
Wildlife and critical ecosystems around the world are protected by over 230,000 legally recognised zones called protected areas. These protected areas range in size from local nature reserves such as at Coldhams Common https://protectedplanet.net/555561770 through to iconic national parks like the Serengeti https://protectedplanet.net/555570276.
Many user groups need access to this data. Whether they are global extractives businesses looking to avoid environmentally sensitive areas when planning concession sites, academic researchers in the field wanting to know how a specific species interacts with the protected area network, or tourists looking for a local beauty spot.
Whilst these areas can be visualised geospatially on https://protectedplanet.net, there’s currently no quick way to discover your current proximity to nearby protected areas. Furthermore, the data is limited to technical information about the size and classification of the site. There is no information about what’s inside any given protected area. Is it barren scrubland or dense forrest? Is there evidence of agriculture where there shouldn’t be, or has the area been flooded? This extra level of information is extremely useful for conservationists (including park managers) to track the health of a protected area.
Your task is to build a mobile application that enables the user to quickly orientate themselves with nearby protected areas, and serve vital contextual data about those areas by accessing other services. You should specifically include data from the European Space Agencies Global Land Cover Layer to show the percentage of cover types within the protected area (e.g. 40% Mosaic vegetation, 60% Mosaic grassland), and you may include other information such as geolocated photos from Flickr, or species from the IUCN red list of threatened species.
Ideas for 2017:
Revised ideas
for "Finding Nature"
The European Space Agency publish a global land cover map. Students could use this to show statistics on the type of land cover and vegetation across a given protected area (e.g 40% Mosaic Grassland, 10% bare areas). Its available via an API supplied by https://ramani.ujuizi.com/ (you can view the data here https://ramani.ujuizi.com/maps/index.html#, and the classifications here https://ramani.ujuizi.com/maps/layer_simS3seriesCoverGlobal_info.html) but also directly from the ESA http://due.esrin.esa.int/page_globcover.php if they want to get into some web GIS.
Using the ESA land cover map seems like a great idea. Mashing this up with the protected areas database should indeed be about the right level of complexity.
What might the functional goal be? To support activism or lobbying, or perhaps eco-tourism, or citizen monitoring of development projects? For students who have only a vague engagement with current affairs, it may be necessary to give a fairly heavy hint about the benefits that could be delivered by the novel technical capability.
Finding Nature
Wildlife and critical ecosystems around the world are protected by over 230,000 legally recognised zones called protected areas. These protected areas range in size from local nature reserves such as at Coldhams Common https://protectedplanet.net/555561770 through to iconic national parks like the Serengeti https://protectedplanet.net/555570276.
Many user groups need access to this data. Whether they are global extractives businesses looking to avoid environmentally sensitive areas when planning concession sites, academic researchers in the field wanting to know how a specific species interacts with the protected area network, or tourists looking for a local beauty spot.
Whilst these areas can be visualised geospatially on https://protectedplanet.net, there’s currently no quick way to discover your current proximity to nearby protected areas.
Your task is to build a mobile application that enables the user to quickly orientate themselves with nearby protected areas, and serve vital contextual data about those areas by accessing other services such as the IUCN list of threatened species http://www.iucnredlist.org/ or geolocated photographs of the area.
Consider what other information might be useful (and useable) in a mobile context such as direction & distance to the boundary, or is available through open data initiatives such as https://openoil.net/
Feedback:
A clear and straightforward application, but slightly below the level of technical ambition that we typically aim for. It may well be possible to think of an enhancement - for example, integrating with climate data to assess effects of industrial pollution or infrastructure projects resulting from prevailing windspeed.
Unearthing the scale of illegal wildlife trade facilitated by the web
The black market for illegal wildlife products is worth an estimated $19billion USD a year putting it on a par with drugs, human trafficking and arms dealing in terms of scale.
Much of this trade is facilitated on-line either by traders knowingly breaking the law, or innocent (if misguided) consumers buying items without realising they are fuelling international crime and pushing many species to the brink of extinction.
Your task is to analyse social media and online market places (such as eBay and Etsy) to identify possible illegal wildlife trade in specific items (such as ivory) or species in order to establish a credible, defensible figure on the scale of the problem. How will you overcome simple problems such as those people posting to raise awareness of wildlife crime, and those facilitating it?
Once you have an efficient way of identifying the number of posts / adverts relating to illegal wildlife crime within a given time period, what other insights can you identify? Where are the biggest markets (geographically and on-line) for example?
Feedback:
Would the CITES database be the best reference source for a list of species that might be traded? It is easy enough to connect “ivory” with “elephant”, but how might other species be connected with products that are made from them? One possibility would be to use wordnet to establish relationships between animals and sales descriptions, but I suspect it does not have good coverage of exotic species. Another would be to scan Wikipedia pages for endangered species, in order to find terms that might appear in advertisements. Is this the kind of thing you had in mind? This becomes rather more challenging, though we could perhaps build on Microsoft Cognitive Services APIs.
2016 proposal
Strategic Selection
Some species are endangered, but not very genetically interesting, while some very interesting species are (fortunately) rather common. International legislation and conservation efforts should be prioritised accordingly, but there is so much data to be taken into account that it is hard to decide where to expend one’s efforts.The http://www.speciesplus.net/ application provides taxonomy, legislation, distribution and trade information on controlled species (which you can search filter and download), and the Trade Database, http://trade.cites.org/ gives historic trade data on all animals traded and which are covered under the Convention on International Trade of Endangered Species of Wild Flora and Fauna (CITES). Your task is to create an evolutionary family tree that, rather than emphasising natural selection, helps users navigate toward branches of the tree that need strategic selection.
early ideas
We work on data visualisations, on-line geospatial mapping and taxonomic databases, generally focused on trying to communicate complex data to non-specialists (policy & decision makers), so we believe in taking a strong user centred development approach to our work.
In terms of taxonomic databases a good place to start would be our http://www.speciesplus.net/ application which lists taxonomy, legislation, distribution and trade information on controlled species (which you can search filter and download), and the Trade Database, http://trade.cites.org/ which gives historic trade data on all animals traded and which are covered under the Convention on International Trade of Endangered Species of Wild Flora and Fauna (CITES). Again, you can filter and download this data freely.
