Advanced network for the distribution of endangered species

Transcription

1 Advanced network for the distribution of endangered species Cristián Bonacic & Andrea Vásquez PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE Sao Paulo May 2013

2 The problem: Wildlife Data Challenges in Latin America We are facing an era of data-intensive science (Jim Gray s 4 th paradigm of scientific exploration), but Wildlife species are not easy to detect (data generation challenge) Poor technological training (knowledge challenge) Institutions are not coordinated (data sharing challenge)

3 Data Generation Challenge We still know little about many endangered species Many animals are not easy to detect (cryptic behavior, low population density, inaccessible habitats) Extensive surveys and adequate equipment are expensive Citizen science may play a role: Outdoors visitors to natural areas

4 Knowledge Challenge Government officers lack of technology and skills to upload data There are training and language barriers to use available computational tools (e.g. GIS, computational models)

5 Data Sharing Challenge Government agencies have little experience sharing wildlife data and sometimes run parallel database systems for the same topic Sharing protocols are absent Cloud computing is almost none Many useful studies end up stored in papers or s without being utilized Citizen science records of wildlife are not considered.

6 Live ANDES (Advanced Network for Distributions of Endangered Species)

7 Live ANDES: Advanced Network for Distributions of Endangered Species Scientists access to more data for analyses and can share results with policy makers Citizen science Citizens learn about wildlife and gather data for research and management Officials and policy makers can manage their data, share it among them and have feedbacks with the scientific community & citizens

8 Live Andes (Advanced network for the distribution of endangered species ) Wildlife sighting Uploading Sharing Visualization BASELINE (backward) PREDICTIONS (Forecasting) Bonacic, Neyem, Puente, Casas, Vasquez, Petitpas, Zumaeta et al., 2013

9 Using Live Andes Wildlife data Data from EIAs Searchable database Forecasting BASELINE (backward) PREDICTIONS (Forecasting)

10 LOADING DATA FROM EIAs database

11 RESULTS Two species are the most commun 45 species in 14 regions One region has more records than the others Most commun species: Liolaemus tenuis y Tachymenis chilensis

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13 MVC web application Live ANDES (Advanced Network for Distributions of Endangered Species) Database Today records 840+ species 300+ users What s next North America and Bolivia Insects, sea life, plants Challenges Database expansion No-relational?

14 Live ANDES (Advanced Network for Distributions of Endangered Species) Online MVC Web Application Desktop/Laptop Windows Communication Fundation Online/Offline Architecture Smartphones

15 MVC web application Live ANDES (Advanced Network for Distributions of Endangered Species) Data Visualization Geocoded data Analysis Data Mining

16 MVC web application Live ANDES (Advanced Network for Distributions of Endangered Species) Sightings Users & Security Polygons WCF API Statistics Visualization

17 Live ANDES (Advanced Network for Distributions of Endangered Species) Future challenges From in-house hosting to Azure

18 Live ANDES (Advanced Network for Distributions of Endangered Species) Main Challenges

19 Main Challenges Live ANDES (Advanced Network for Distributions of Endangered Species) Online/Offline Cloud Services Smartphones

20 Live ANDES (Advanced Network for Distributions of Endangered Species) Cloud Geocoded Data + audio + video + sound Data Analysis Cloud Services

21 Live ANDES NYC (Advanced Network for Distributions of Endangered Species)

22 Live ANDES Team Research team Dr. Cristian Bonacic Robert Petitpas Catalina Zumaeta Software Development Dr.Andrés Neyem Andrea Vasquez