Data Science, Data Analytics, Big Data, Predictive Analysis, Artificial Intelligence, Generative AI, Large Language Models like ChatGPT, Bard etc. are buzzwords of our times. Big Corporations and many Technological Start-Ups are harnessing Data Science and related technologies for providing new products/services, increasing their productivity, reducing their cost and maximizing their profits. Wildlife Law Enforcement Agencies also need to embrace Data-Driven Risk-Based Approach to enhance their response to wildlife crimes.

Conventional measures to combat wildlife crimes such as on-foot, vehicular & UAV (Drones) patrolling in protected areas; camera traps, radio-collaring of wildlife; scanning of luggage/cargo at railway stations, airports, sea ports, land customs stations, courier terminals, foreign post offices etc.; technical intelligence, human intelligence, cyber patrolling; random physical examination of vehicles, cargo, containers etc. need to be complemented with Data Science and related emerging technologies for transitioning from Traditional Wildlife Law Enforcement to more effective Smart Wildlife Law Enforcement.

Data Science can be applied for Wildlife Law Enforcement in following ways:

• Maintenance & Analysis of Wildlife Crimes & Criminals Database: Maintaining Wildlife Crimes & Criminals Database helps law enforcement officials in gaining insight into various dimensions of willdife crimes such as flora & fauna species being illegally harvested/poached and illegally traded on natural landscape & digital landscape; mapping of hotspots for poaching/illegal harvesting/illegal trade; profile of poachers/traffickers i.e. their names, age, sex, addresses, socio-economic-educational status, details of identity proofs such as Passports & other National Identity Proofs; different modus operandi used for poaching/illegal trade/smuggling; various transportation methods used for wildlife trafficking; air, marine & land routes used for wildlife trafficking; motives to buy wildlife contrabands such as superstitious beliefs, black-magics, for use in traditional medicinal systems, status symbol attached to a particular wildlife/wildlife product; identifying various payment methods and channels across supply chain of wildlife trafficking; cultural practices like wearing body part of a particular wildlife on wedding or particular religious functions. Analytical study of such database through AI enabled Big Data Analytical Tools may be of immense help in identifying trends & patterns of wildlife crime; in generating predictive intelligence for prevention of crime; optimization of available human & other resources; in devising a mult-pronged strategy to mitigate the socio-economic-educational-cultural factors fueling wildlife crimes and in bringing all stakeholders on board for a united fight against the wildlife trafficking.
• Integration of Wildlife Crime Databases & other Crimes' Databases and Analysis of Multi-Agency Crime Data-sets: In countries with federal structure of governance, various states/provinces maintain their own wildlife crime database. Similarly in multi-linguistic countries, states/provinces maintain their database in different languages. Data Science with related technologies like Artificial Intelligence, Generative AI, Machine Learning, Deep Learning and Natural Language Processing (NLP), ETL (extract, transform, load) and ELT (extract, load, transform) etc. can be harnessed for integrating such diverse data-sets and extract relevant information in a language of choice irrespective of language of source database. Similarly, different law enforcement agencies maintain their own data-sets which can also be integrated and analysed using data science and related technolgies to find out convergence of wildlife crimes with other serious organized crimes. This will help in identifying broader criminal networks dealing in various contrabands resulting in synergy between different enforcement agencies to combat this convergence of crimes. For example, a 2018 U.S. Intelligence Community analysis of multi-agency crime data on East Africa confirms substantial convergence of wildlife crime with other serious crimes, especially drug trafficking. That analysis found that more than two-thirds of the actors in the wildlife crime dataset overlapped with individuals and facilitators in the narcotics dataset; about a dozen overlapped with actors in terrorist dataset and a handful overlapped with actors in the proliferation of nuclear related materials dataset. (https://www.worldwildlife.org/pages/tnrc-blog-understanding-crime-convergence-to-better-target-natural-resource-corruption). Creation of a Global Centralized Wildlife Crime Database, with data from customs administrations and other national/international agencies, under the aegis of International Consortium on Combating Wildlife Crime (ICCWC) may be considered by Parties to United Nations on a secure web platform for exchange of information of wildlife crimes & criminals for  countering transnational wildlife trafficking effectively.
• Creation & Analysis of Wildlife Genetic Database: Wildlife law enforcement officials often grapple with the problem of identiication of species to ascertain whether they are protected under law or not. Similarly establishing the geographical origin of seized wildlife/their body parts/products is another challenge to ascertain the jurisdiction of law enforcement agencies for investigation of the wildlife crime and prosecution of the wildlife criminals, more so in case of inter-state and transnational wildlife trafficking. To overcome these challenges data science can be applied for creation and analysis of genetic database of all flora and fauna species found in a particular jurisdiction.
• Combating Wildlife Cyber Crimes through Data Science: In case of wildlife cyber crimes, data science, through data mining & web scraping, can assist in identifying social media & e-commerce sites, darknet sites, other websites & online forums being misused for illegal wildlife trade. Data science can assist law enforcement in identifying wildlife species/products bing offered for sale through keywords, images & videos analysis. By applying Natural Language Processing technique, data science can also perform sentiment analysis on the basis of  discussion related to wildlife illegal trade on cyber space by analyzing text. Data science can also assist in analyzing details like metadata of images/videos of wildlife contrabands posted on cyberspace, IP Addresses, mobile numbers, Email IDs & other credentials of wildlife cyber criminals obtained from digital platforms and in strategising legal course of action against them.
• Data Science for Data-Integrity & Secure Exchange of Informations between Wildlife Law Enforcement Agencies: Data science with Blockchain Technology can also help in building a Digital Ecosystem to insure data-integrity of wildlife crime database and secure exchange of infomation between authorized law enforcement authorities without compromising the data-protection and data-privacy laws.

How India is Leveraging Data Science for Wildlife Law Enforcement: 

• Leveraging data science and artificial intelligence Indian Customs has put in place National Customs Targeting Centre  for Cargo (NCTC-Cargo) and National Customs Targeting Centre for Passenger (NCTC-Pax) for risk analysis and targeting of risky cargo and passengers crossing the borders on the basis of various risk parameters taken from historical crime data. These risk parameters are regularly updated and refined on the basis of inputs received from law enforcement officials who detect new patterns/modus operandi of crimes including wildlife crimes.
• India is a Union of States. Different States have developed their own wildlife crime database such as Kerala has developed the Hostile Activity Watch Kernel (HAWK) database.
• At Federal Government level Wildlife Crime Control Bureau has developed Centralized Wildlife Crime Data Bank. All States' Forest/Wildlife Departments and Police Departments have been provided log-in credentials to access and feed the data in this Centralized Data Bank in respect of wildlife crimes in their States.
• Crime and Criminal Tracking Network & Systems (CCTNS) captures the crime (including wildlife crime) and criminal data across the police stations in the country.
• Inter-operable Criminal Justice System (ICJS) aims to integrate CCTNS (Crime and Criminal Tracking Network and System) project with eCourts and ePrisons database and also with other pillars of criminal justice system such as forensic labs, prosecution etc in order to build an effective criminal justice system database across the country.
• National Intelligence Grid or NATGRID is National Master Database which connects databases of various Enforcement Agencies for intelligence and information sharing amongst them.

In conclusion, the fusion of data science and wildlife law enforcement is the way forward for taking wildlife trafficking head-on and dismantling the wildlife criminal networks.