PROJECT 1

DISEASE VECTOR MAPPING OF MALARIA IN BOR COUNTY, SOUTH SUDAN

1. Challenge:
   Bor County in South Sudan faces a significant malaria burden, particularly during the wet season, impacting vulnerable populations such as pregnant women and children under five. The primary challenge was the lack of updated, comprehensive data on the distribution of malaria vectors and the correlation between environmental and climatic factors – such as temperature, precipitation, elevation, slope, and proximity to wetlands – that influence mosquito breeding patterns. This gap in data hindered effective targeting of malaria control interventions.
2. Solution Using GIS/Remote Sesnsing:
   Remote Sensing & GIS Techniques: Google Earth Engine (GEE) and ArcGIS were utilized for spatio-temporal analysis of malaria vector habitats. These geospatial tools enabled mapping and analysis of critical environmental factors influencing malaria transmission, such as land use, soil type, elevation, and climatic conditions like temperature, precipitation, and humidity.
   Weighted Suitability Analysis: A weighted overlay analysis was performed to assess the relative importance of each environmental factor and generate malaria risk maps for both the dry and wet seasons. The analysis covered data from 2021 to 2023, providing an accurate seasonal variation in vector distribution.
   Geospatial Data Processing: High-resolution satellite imagery from Sentinel-2 and Landsat 8 was processed in GEE, where the data was analyzed and exported to ArcGIS for further reclassification and overlay analysis. This allowed for malaria vector breeding, considering both environmental and climatic data.
3. Outcome/Results:
   Malaria Risk Mapping: The project produced detailed seasonal malaria risk maps, revealing a significant increase in breeding sites during the wet season. These maps identified regions with the highest risk, especially those near wetlands and lower elevations.
   Targeted Intervention Insights: The results highlighted key areas for targeted malaria interventions, including the distribution of mosquito nets and setting up health facilities in high-risk areas during peak transmission times. The data allowed for more precise planning of resources to mitigate the impact of malaria.
   Public Health Strategy Impact: The findings offer valuable insights for policymakers in South Sudan, enabling more effective resource allocation for malaria prevention. By focusing on high-risk zones identified through geospatial analysis, the incidence of malaria in Bor County can be reduced, especially during peak transmission periods.

For additional project maps, please click on the link below:
https://www.terrasynthgeospatial.com/maps/

PROJECT 2

USING GIS AND REMOTE SENSING TO EVALUATE AND OPTIMIZE FIRE-FIGHTING SERVICES IN NAIROBI, KENYA

1. Challenge:
   Nairobi County’s fire services are under-equipped and inefficient, with only two fully operational fire stations serving over 5 million people. The city’s densely populated informal settlements are prone to frequent fires, which result in significant loss of life and property damage. The slow response times are exacerbated by poor infrastructure, including limited road access, inadequate fire hydrants, and a lack of strategically placed fire stations.
2. Solution Using GIS/Remote Sensing:
   Mapping Fire Resources: GIS technology was used to map the locations of existing fire stations, road networks, health facilities, and fire hydrants. Remote sesing data from Google Earth Engine and ArcGIS was leveraged to visualize and assess the distribution and availability of these essential resources.
   Network Analysis: GIS tools such as proximity and overlay analysis were used to conduct route analysis, identifying the quickest and most efficient paths for fire-fighting vehicles to reach fire hotspots. The analysis highlighted the deficiencies in the existing infrastructure, including poorly connected road networks and insufficient coverage in high-risk areas.
   Site Suitability Analysis: GIS-based spatial analysis was used to propose new fire station locations in underserved areas like Lang’ata, Kasarani, and Embakasi East. Factors such as population density, road access, and proximity to high-risk zones were considered to ensure that these new stations would optimize fire response times.
   We Application Development: A GIS-based web application was developed to support fire-fighting teams by optimizing route planning and integrating real-time data on health facilities and hydrant availability. This tool allows emergency personnel to make informed decisions based on the most accurate data.
3. Outcome/Results:
   Improved Resource Allocation: The project successfully identified key areas where new fire stations were most needed, particularly in the informal settlements that are most prone to fires. The analysis also pointed to the necessity of upgrading road infrastructure and increasing the number of fire hydrants to ensure more effective fire-fighting responses.
   Strategic Recommendations: Based on the analysis, recommendations were made to increase the number of fire stations, enhance fire-fighting equipment, improve road infrastructure, and establish designated health facilities as evacuation points during emergencies.
   Impact on Public Safety: The integration of GIS and remote sensing technologies can lead to optimized fire-fighting services, potentially reducing response times and property damage. It demonstrated the significant value of geospatial technologies in improving public safety by ensuring a more efficient and effective emergency response system.

For additional project maps, please click on the link below:
https://www.terrasynthgeospatial.com/maps/

PROJECT 3

CONSULTATION FOR GEOSPATIAL AND GENOTYPIC DISTRIBUTION OF DELAYED AND NON-DELAYED DIAGNOSIS OF MYCOBACTERIUM TUBERCULOSIS AMONG TB PATIENTS ATTENDING SELECTED HEALTH FACILITIES IN NAIROBI COUNTY, KENYA (PhD RESEARCH)

1. Challenge:
   The PhD researcher faced a challenge in understanding the factors contributing to the delayed diagnosis of tuberculosis (TB) in Nairobi County. The lack of integrated geospatial, genotypic, and sociodemographic data made it difficult to assess the spatial distribution of delayed and non-delayed diagnoses and identified potential factors contributing to these delays. The researcher sought expert consultation to incorporate GIS and spatial analysis into their study to address this gap.
2. Solution Using GIS/Remote Sensing:
   Consultation on Geospatial Mapping: We were consulted to provide expert advice on mapping the spatial distribution of delayed and non-delayed TB diagnoses. GIS tools were employed to analyze how spatial factors (e.g., proximity to health facilities, population density) influenced diagnosis delays.
   Genotypic Data Integration: Based on the consultation, the project incorporated the integration of genotypic data of Mycobacterium tuberculosis strains with spatial analysis to investigate correlations between specific strains and delays in diagnosis.
   Spatial Analysis of Sociodemographic Factors: We helped design a spatial analysis of sociodemographic factors, such as income, education level, and access to healthcare, to identify geographic patterns and associations with diagnosis delays.
   Data Visualization and Reporting: We assisted the researcher in presenting the results using GIS-based maps and visualizations to help communicate the findings effectively to the study’s stakeholders.
3. Outcome/Results:
   Enhanced Research Methodology: The consultation provided the researcher with the necessary GIS tools and methodologies to integrate geospatial and genotypic data, enriching the quality and scope of the research. This approach allowed for a more comprehensive analysis of the factors contributing to delayed TB diagnosis.
   Spatial Insights for Health Interventions: The project’s outcomes highlighted specific areas where delayed TB diagnoses were more prevalent, offering valuable insights for targeted health interventions. The integration of genotypic and spatial data also revealed strain-specific factors influencing diagnosis delays.
   Impact on Public Health Strategy: The findings, made possible through GIS consultation, offer actionable recommendations to policymakers and health practitioners in Nairobi County. The study’s insights will guide public health strategies, such as optimizing healthcare resources and interventions based on spatial and genetic patterns, improving TB diagnosis and treatment outcomes.

PROJECT 4

GEOSPATIAL CONSULTATION FOR MINING EXPLORATION IN MARIDI, SOUTH SUDAN (SUE INVESTMENTS LTD.)

1. Challenge:
   Sue Investments Ltd. sought to present a compelling proposal to potential investors for a mining exploration project in Maridi, South Sudan. The challenge was to provide a comprehensive geospatial analysis that would aid in understanding the potential for mineral extraction, identify key exploration areas, and ensure safe and efficient access to these sites. There was a need to establish a robust geodatabase to manage sampling sites and analyze mineral distributions in the region.
2. Solution Using GIS/Remote Sensing:
   Topographic Mapping: We created detailed topographic maps of the Maridi region to clearly outline the areas targeted for exploration. These maps provided a visual representation of the terrain and elevation, essential for planning the exploration process.
   Land Use/Land Cover (LULC) Analysis: We performed LULC analysis to assess the land characteristics and suitability for minng activities, utilizing satellite imagery and GIS tools to classify and analyze the land types in the area.
   Soil Type and Mineral Analysis: Using opensource remote sensing data, we conducted soil type distribution analysis, identifying areas with probable mineral presence based on the soil characteristics.
   Sample Site Selection and Terrain Analysis: Based on the identified mineral types, we selected specific areas for sampling. We used GIS to map out the best routes to these sampling points, ensuring safe and efficient navigation through the terrain.
   Geodatabase Development: We created a geodatabase to store detailed records of the sampling sites, the results from each site, and the related geological data, ensuring proper management of the exploration data.
   Surface Modelling: Using the sampling results, we developed surface models to predict the likely distribution of mineral types in the region. This allowed us to identify high-potential areas for further exploration.
   Geodatabase Updates: We continually updated the geodatabase with new sampling data and analysis results, ensuring that the exploration records remained accurate and up to date.
3. Outcome/Results:
   Comprehensive Exploration Proposal: The geospatial analyses provided Sue Investment Ltd. with a solid foundation for their proposal to potential investors. The topographic maps, LULC analysis, and mineral distribution models helped to illustrate the potential for mineral extraction in the Maridi region.
   Efficient Planning and Sampling: The sample site selection and terrain analysis enabled efficient planing for further exploration and sample collection, reducing risks associated with navigating difficult terrain.
   Data-Driven Decision Making: The geodatabase and surface models empowered Sue Investments Ltd. with accurate, reliable data that could guide future exploration decisions and increase investor confidence in the project’s feasibility.
   Investors’ Confidence: By incorporating geospatial insights into the proposal, Sue Investments Ltd. significantly enhanced their chances of attracting investment, as the data-driven approach demonstrated a well-researched, thorough exploration plan.