Geography- GIS | GeoHealth | Remote Sensing | Spatial Statistics | Geospatial Artificial Intelligence (GEOAI) | Python | Web Mapping | QGIS | ArcGIS Pro
A geospatial scientist in training, passionate about the intersection of GIS, GeoAI, and healthcare. My work focuses on how spatial data and machine learning can be harnessed to improve public health systems and resilience. I’m currently building my research foundation in spatial statistics, geospatial programming, and healthcare GIS with the vision of developing scalable, data-driven solutions for global health challenges.
My name is Theresa Nyatichi Monari.
A geospatial researcher passionate about applying GIS, remote sensing, and spatial statistics to solve pressing healthcare and public health challenges.
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!
