John Wahome Gathoni

I recently worked on an ArcGIS Dashboard to visualize shop sales in the Nairobi Metropolitan area. This dashboard includes data on randomly generated shops and their sales from January to May 2024.

1. Generating Data in QGIS I used QGIS to generate random data for shops and their sales. Shops: Randomly placed within all wards. Sales: Monthly sales data for each shop from Jan - May 2024. Geographical Boundaries: Ward boundaries.

2. Setting Up ArcGIS Next, I imported the data into ArcGIS Online. Importing Data: Uploaded the shop locations and sales data, as well as the geographical boundaries. Creating Layers: Set up different layers for shops and wards.

3. Designing the Dashboard I used ArcGIS Dashboards to put everything together. Components of the Dashboard:

Map: A central map displaying shop locations and ward boundaries in the Nairobi Metropolitan area. Shops are marked with points, and wards are outlined.

Indicators: Total 2024 Sales: Displays the total sales from January to May 2024. Number of Shops: Shows the total number of shops. Number of Wards: Indicates the number of wards in the area. Number of Subcounties: Displays the number of subcounties.

Filters: County Filter: Allows users to filter data by county. Subcounty Filter: Allows users to filter data by subcounty. Wards Filter: Allows users to filter data by wards. Overall Sales Filter: Users can filter based on sales figures.

Graphs: Sales by Month: A line graph showing sales trends from January to May 2024. Sales by County: A bar chart comparing sales across different counties. Sales by Subcounty: A bar chart comparing sales across different subcounties.

This study was meant to ensure that there is proper and efficient conservation of soil and water using geospatial tools to enable us identify priority areas to carry out conservation. Over the past years, various fields of study have established how critical it is to conserve these natural resources in the ecosystem and to ensure sustainability in not only green livelihoods but also to enhance living conditions of the life on earth. The aim of this research was to generate high priority sites for establishing soil and water conservation techniques in the Lower Bogoria Landscapes in Baringo, Kenya using GIS-based multicriteria decision analysis. Various criteria were analyzed to generate the final conservation priority sites, such as land use land cover, rainfall runoff, soil erosion and slope. The criteria were assigned weights using the AHP technique and overlayed using the weighted overlay tools to produce the final outputs. Land use land cover maps were generated using supervised maximum likelihood technique, rainfall run-off maps were generated using the SCS-CN method and soil erosion maps were generated using RUSLE model. The final soil and water conservation maps showed that high and moderate priority areas requiring the establishment of techniques and mechanisms to control soil erosion and conserve water increased from 1990 to 2020. In 2020, more than 50% of the total study area was classified as moderate to high priority for water and soil conservation.

This research focuses on mapping geo-hazard risk zones of the region. The risk zones were developed from a combination of land use land cover maps, agroecological zones maps and soil erosion maps using the Analytical Hierarchy Process method of multi-criteria analysis. The final results depict the geohazard risk maps which show the susceptibility of different areas in the catchment (classified as risk zones) to hazards. The zones range from no risk zones to very high-risk zones. The results showed that the lowlands are most susceptible to hazards as they were classified as high-risk zones.