Home » Chapter 22 : Geospatial Analysis
Geospatial Humanities Project Ideas
There are many options for Geospatial projects in the Humanities.
- Ancient Script Decipherment Tool:
- Description: Create a tool to decipher and geographically analyze ancient scripts.
- Python Role: NLP and pattern recognition.
- Suggested Libraries: TensorFlow, Keras, spaCy.
- Ancient Water Systems Analysis:
- Description: Study and map ancient water systems like aqueducts and irrigation channels.
- Python Role: Spatial data analysis and visualization.
- Suggested Libraries: GeoPandas, Rasterio, Matplotlib.
- Archaeological Site Prediction Using Machine Learning:
- Description: Predict potential archaeological sites using machine learning on geographical data.
- Python Role: Machine learning and spatial analysis.
- Suggested Libraries: scikit-learn, Rasterio, Geopandas.
- Cultural Event Geographic Impact Analysis:
- Description: Analyze the geographic impact of major cultural events, festivals, or gatherings.
- Python Role: Spatial data analysis and visualization.
- Suggested Libraries: GeoPandas, Matplotlib, Seaborn.
- Cultural Heritage Risk Modeling:
- Description: Model risks to cultural heritage sites due to environmental changes.
- Python Role: Risk analysis using spatial data.
- Suggested Libraries: scikit-learn, GeoPandas, NumPy.
- Climate Impact Analysis on Heritage Sites:
- Description: Study how climate change affects heritage sites over time.
- Python Role: Data analysis and visualization.
- Suggested Libraries: Pandas, Matplotlib, Basemap.
- Cultural Landscape Analysis Using Remote Sensing:
- Description: Use remote sensing data to analyze and visualize cultural landscapes.
- Python Role: Satellite data processing and analysis.
- Suggested Libraries: Rasterio, NumPy, Matplotlib.
- Digital Reconstruction of Lost Cities:
- Description: Use historical records and maps to digitally reconstruct ancient or lost cities.
- Python Role: 3D modeling and historical data integration.
- Suggested Libraries: PyVista, Pandas, Matplotlib.
- Ethnographic Data Interactive Map:
- Description: Create interactive maps to explore ethnographic data.
- Python Role: Web mapping and data visualization.
- Suggested Libraries: Flask, Folium, Pandas.
- Folklore Origin Mapping:
- Description: Map the origins and spread of different folklore stories.
- Python Role: Text mining and spatial analysis.
- Suggested Libraries: NLTK, GeoPandas, Folium.
- Digital Elevation Model Analysis of Archaeological Sites:
- Description: Use digital elevation models to explore and analyze archaeological sites.
- Python Role: Terrain analysis and 3D visualization.
- Suggested Libraries: GDAL, NumPy, Matplotlib.
- Geographic Patterns in Historical Elections:
- Description: Analyze spatial voting patterns in historical elections.
- Python Role: Data analysis and geospatial visualization.
- Suggested Libraries: Pandas, PySAL, Folium.
- Geographical Analysis of Folk Music Origins:
- Description: Explore the origins and spread of folk music using geospatial analysis.
- Python Role: Audio data analysis and spatial mapping.
- Suggested Libraries: Librosa, GeoPandas, Matplotlib.
- Geospatial Analysis of Colonial Cartography:
- Description: Analyze colonial-era maps for insights into historical perceptions and map-making biases.
- Python Role: Image analysis and spatial data integration.
- Suggested Libraries: OpenCV
- Geospatial Analysis of Mythological Texts:
- Description: Analyze locations and geographies mentioned in mythological texts.
- Python Role: Text mining and spatial analysis.
- Suggested Libraries: NLTK, GeoPandas, Folium.
- Geospatial Literary Analysis:
- Description: Map the settings and journeys in literary works.
- Python Role: Text analysis and mapping.
- Suggested Libraries: Geopandas, NLTK, Folium.
- Historical Battlefields Simulation:
- Description: Simulate historical battlefields and analyze strategies.
- Python Role: Geospatial modeling and visualization.
- Suggested Libraries: QGIS with Python, GeoPandas, Matplotlib.
- Historical Land Use Change Analysis:
- Description: Analyze changes in land use over time through historical maps.
- Python Role: Image analysis and spatial data processing.
- Suggested Libraries: OpenCV, GeoPandas, Matplotlib.
- Historical Landmark Photo Geotagging:
- Description: Collect and geotag historical photos of landmarks for digital archives.
- Python Role: Image processing and geotagging.
- Suggested Libraries: Pillow, ExifRead, Folium.
- Historical Map Digitization and Overlay:
- Description: Digitize historical maps and overlay them on modern maps to analyze geographic changes over time.
- Python Role: Image processing and geospatial analysis.
- Suggested Libraries: OpenCV, GeoPandas, Shapely.
- Historical Pollution Mapping:
- Description: Map and analyze pollution levels in historical industrial areas.
- Python Role: Data analysis and geospatial visualization.
- Suggested Libraries: Pandas, Matplotlib, Folium.
- Historical Trade Routes Network Analysis:
- Description: Analyze historical trade routes using network theory.
- Python Role: Network analysis in a spatial context.
- Suggested Libraries: NetworkX, GeoPandas, PySAL.
- Historical Weather Data Visualization:
- Description: Visualize historical weather patterns and their impacts on various regions.
- Python Role: Data analysis and climatology visualization.
- Suggested Libraries: xarray, Matplotlib, Cartopy.
- Historical Urban Planning Analysis:
- Description: Study the evolution of urban planning in historical cities.
- Python Role: Geospatial data processing and analysis.
- Suggested Libraries: GeoPandas, PySAL, Matplotlib.
- Interactive Atlas of Historical Linguistics:
- Description: Create an interactive digital atlas for exploring the evolution of languages and dialects.
- Python Role: Interactive web mapping and data visualization.
- Suggested Libraries: Dash, Plotly, GeoPandas.
- Interactive Map of Historical Trade Goods:
- Description: Create an interactive map showing the historical trade routes and goods.
- Python Role: Data visualization and interactive mapping.
- Suggested Libraries: Bokeh, Pandas, GeoPandas.
- Linguistic Geography Analysis:
- Description: Analyze the geographical distribution of languages or dialects.
- Python Role: Spatial data visualization.
- Suggested Libraries: GeoPandas, Matplotlib, Contextily.
- Mapping Ancient Farming Practices:
- Description: Map and analyze ancient farming practices and their geographical distribution.
- Python Role: Spatial data analysis and historical agriculture research.
- Suggested Libraries: GeoPandas, Pandas, Matplotlib.
- Mapping Historical Language Shifts:
- Description: Map and analyze the shifts in language usage over time and regions.
- Python Role: Geospatial data processing and visualization.
- Suggested Libraries: GeoPandas, Matplotlib, Seaborn.
- Maritime History Exploration Tool:
- Description: Explore maritime history through ship logs and routes.
- Python Role: Data parsing and spatial visualization.
- Suggested Libraries: Pandas, Matplotlib, Basemap.
- Migration Patterns Visualization:
- Description: Visualize historical migration patterns and trends.
- Python Role: Spatial data manipulation and mapping.
- Suggested Libraries: PySAL, Folium, GeoPandas.
- Public Health in History Mapping:
- Description: Map historical public health data to understand past epidemics.
- Python Role: Data analysis and geospatial visualization.
- Suggested Libraries: Pandas, GeoPandas, Plotly.
- Reconstruction of Historical Sites:
- Description: Create 3D models of historical sites using geospatial data for virtual tours or educational purposes.
- Python Role: 3D modeling and visualization.
- Suggested Libraries: VTK, PyVista, GeoPandas.
- Spatial Analysis of Historical Documents:
- Description: Analyze the spatial references in historical documents to map historical events or places.
- Python Role: Text mining and spatial visualization.
- Suggested Libraries: spaCy, GeoPandas, Matplotlib.
- Spatial Patterns in Historical Census Data:
- Description: Analyze spatial patterns and trends in historical census data.
- Python Role: Data analysis and mapping.
- Suggested Libraries: Pandas, PySAL, Matplotlib.
- Urban Growth Analysis Tool:
- Description: Analyze urban growth and sprawl over time.
- Python Role: Time-series analysis and spatial data processing.
- Suggested Libraries: Pandas, GeoPandas, PySAL.
