Detecting the Severity of Socio-Spatial Conflicts Involving Wild Boars in the City Using Social Media Data

Area of Science:

• Urban ecology
• Wildlife management
• Geographic Information Systems (GIS)

Background:

• Wild boar (Sus scrofa) populations are increasingly encroaching on urban environments, leading to significant socio-spatial conflicts.
• Traditional methods for assessing these conflicts, such as analyzing spatial data from surveillance and intervention reports, are often labor-intensive and costly.
• There is a need for innovative and efficient methods to monitor and manage urban wildlife-human interactions.

Purpose of the Study:

• To propose and evaluate a novel method for assessing the risk and severity of wild boar encroachment and associated socio-spatial conflicts in urban areas.
• To determine the applicability of crowdsourced big data from social media platforms for identifying the location and nature of these conflicts.
• To bridge the gap in using crowdsourcing data for understanding urban wildlife-human conflicts.

Main Methods:

• Utilized big data, specifically multimedia and descriptive content from social media platforms.
• Applied a photointerpretation method for data analysis.
• Employed the kernel density estimation (KDE) tool within ArcGIS Desktop software for spatial analysis.
• Tested the approach in Olsztyn, Poland.

Main Results:

• The study demonstrated the effectiveness of using social media data to identify and map areas affected by wild boar presence and related conflicts.
• Kernel density estimation effectively visualized conflict hotspots derived from social media data.
• Validation against intervention service reports confirmed the high coverage and utility of the crowdsourced data approach.

Conclusions:

• Crowdsourcing data from social media presents a valuable, cost-effective tool for monitoring urban wild boar conflicts.
• This approach significantly enhances the ability to assess the spatial distribution and intensity of human-wildlife conflicts in cities.
• The findings support the integration of social media analytics into urban wildlife management strategies.