Geography- GIS | Geospatial Artificial Intelligence (GEOAI) | Remote Sensing | Disaster Resilience | GeoHealth | Human Mobility | Urban and Transportation Geography | Web Mapping |
A geospatial scientist in training, passionate about advancing the use of GeoAI to improve disaster resilience, urban and transportation infrastructure, and public health outcomes and equity. I have dedicated myself to building strong technical skills in Python, geospatial analysis, and machine learning while volunteering and pursuing self-directed research preparation.
My name is Theresa Nyatichi Monari. My research interests center on the integration of Geospatial Artificial Intelligence (GeoAI) and Internet of Things (IoT ) to promote faster and near real-time disaster prediction and response frameworks that ensure smart disaster resilience and decision making.
This project aims to analyze and map the Land Use and Land Cover (LULC) change of Siaya county from 2015 to January 2025 using ISO cluster Unsupervised classification method. The analysis utilizes ESRI’s ArcGIS Pro software to classify land cover types, such as built-up areas, vegetational land, and water, as of 2025.
This project aims to analyze and map the Land Use and Land Cover (LULC) of Kisii county in Kenya, East Africa. The analysis utilizes ESRI’s ArcGIS Pro and QGIS software to classify land cover types, such as built-up areas, agricultural land, and forests, as of 2024. The results will support sustainable land management, environmental management, and urban planning.
In this project, Kenya's 2019 population census is analyzed using Python's Pandas, Geopandas. The results are presented in an interactive choropleth map using leaflet.js. Each county's population density can be viewed on the web map by clicking on the specific county.
This project features the 2017 water crisis in Cape Town city in South Africa using pandas and folium libraries. The web maps show the 2017 average water Consumption per suburb in the month of October. Users can select points showing pop-up of the suburb name and water Consumption per kilolitre.
The primary goal of this study was to show how to use a Geographic Information System (GIS) to determine the relationship between CBC school access, school enrollment, and people’s population. The study's methodology included collecting various datasets for schools, populations, and roads in Makadara Sub County, which is part of the larger Nairobi County in Kenya.The proposal for locations of new secondary schools was determined by considering the population in the area, the locations of already established secondary schools, the school enrollment, and the road network in the area.
Check out my current projects and research in GIS, Remote Sensing, GeoAI, etc...
More ResearchTheresa Nyatichi Monari
Email: nyatichitheresa@gmail.com
Please feel free to contact me. I look forward to hearing from you!
