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Emerging Technologies in Geography, : child-background-geography-clipart…
Emerging Technologies in Geography
Impact on Understanding
Significantly improves accuracy and granularity of data on landscapes, urban environments, and ecological systems.
Enables near real-time disaster assessments (e.g., floods, wildfires), allowing faster, more informed responses.
Facilitates exploration of inaccessible or hazardous areas safely.
Reveals complex patterns and changes that shape human-environment interactions with unprecedented clarity.
What is the New Knowledge?
This allows ultra-high-resolution mapping, real-time environmental monitoring, and predictive spatial analytics unheard of previously.
Integration of drones, Artificial Intelligence (AI), and machine learning with Geographic Information Systems (GIS).
Bias ↔ Disaster Response
AI/ML Bias: If training data is skewed toward urban infrastructure, informal settlements or rural housing might be misclassified as “low priority” or not detected at all.
Disaster Response Impact: Rescue efforts may then disproportionately favour well-mapped, wealthier areas → delays or neglect for vulnerable populations.
Concrete Example: In the 2023 Turkey–Syria earthquake, early AI damage-assessment tools had higher accuracy in urban centres than rural border villages, meaning aid distribution was initially uneven.
Shift in Understanding:
Earlier: Technology was assumed to be “neutral” and universally beneficial.
Now: Recognition that algorithmic bias can deepen inequalities during crises, so ethical oversight and diverse datasets are critical.
Changes to Established Values and Ethical Issues
Data privacy concerns as surveillance and monitoring capabilities grow.
Accessibility and equity: Who owns and controls these data and technologies?
Ethical responsibility in data use and ensuring inclusive benefits across communities.
Challenges traditional notions of fieldwork and researcher-subject relationships.
Raises questions around algorithmic bias and transparency in AI-driven geographical modeling.
Branches:
Drones: mapping, monitoring, disaster response
AI & Machine Learning: data analysis, pattern recognition, prediction
GIS Integration: visualization, spatial analytics
Ethical Considerations of Emerging Geographic Technologies
Privacy
Concerns over surveillance and personal data collection using high-resolution drones and satellite imagery.
Need for clear policies on data usage and consent.
Equity
Access to advanced technologies may be limited to wealthier regions or institutions.
Risk of widening digital divides and unequal benefits from geographic research.
Bias
Potential for algorithmic bias in AI-driven spatial data analysis.
Necessity for transparency and critical evaluation of data sources.
Impact on Research and Teaching
New Methodologies
Integration of drones, LiDAR, AI, and GIS allows for detailed, real-time environmental monitoring.
Enables interdisciplinary and big-data approaches to geographical questions.
Skillsets
Geographers must develop competencies in technology use, data analytics, and ethical data management.
Teaching now includes training students in geospatial tech, data literacy, and critical thinking about technology impacts.
values and ethics
Ethical Dimensions
Privacy → Drone imagery can capture personal data.
Equity → Ensure rural/low-resource areas also benefit.
Algorithmic Bias → Risk of AI underrepresenting informal settlements → delays in aid.
Fieldwork Relationships → Need to respect local communities’ agency.
Cross-links (Interconnections)
Bias ↔ Disaster Response: Biased AI can misclassify vulnerable groups → inequitable rescue.
Privacy ↔ GIS: Detailed mapping may expose sensitive household data
Advancing Understanding
Greater Accuracy → AI/ML improves damage classification.
Real-time Response → Drones + GIS accelerate aid delivery.
Real-time Response → Drones + GIS accelerate aid delivery.
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