LMjuju10/Restoring-water-access-Part-4

This project transforms raw water access data in Maji Ndogo into actionable insights to guide resource allocation, budget planning, and the deployment of engineering teams for improving water infrastructure and reducing long queues. # Restoring-water-access-Part-4 ## CHARTING THE COURSE FOR MAJI NDOGO’S FUTURE ### Table of Contents Project Overview Data Source Exploratory Data Analysis Data Analysis Insights and Summary --- ### Project Overview In this phase, we transform our raw data into meaningful insights for decision-makers. This process allows us to identify necessary materials, plan budgets effectively, and prioritize areas requiring immediate action. We don't just analyze data; we make it accessible and actionable for everyone involved in this mission. Finally, we create data-driven job lists for our engineers, ensuring they have clear directions to effectively tackle the challenges ahead in fixing the infrastructure in Maji Ndogo. --- ### Data Source `Md_water_services` --- ### Exploratory Data Analysis Key questions guiding the analysis: 1.Which town has the highest ratio of people who have taps, but have no running water? 2.What is the distribution of water sources (river, tap, well, shared tap) across different provinces in Maji Ndogo? 3.How does wealth disparity correlate with access to safe drinking water in Sokoto? 4. Which towns or regions have the highest percentage of non-functional tap infrastructure, and what are the primary reasons for these malfunctions (e.g., pipes, pumps, reservoirs)? 5.How do the proposed intervention strategies (drilling wells, fixing existing infrastructure, installing filters, increasing shared taps) align with the goal of reducing water queue times to under 30 minutes? --- ### Data Analysis **The data analysis process included:** - Breaking down our data into provinces and towns by source type to analyze the percentages of each water source in each town. This was achieved by aggregating the data and using joins on the various tables and Conditional Filters. - Creating a Pivot table in order to visualize this. - Creating views to analyze the data in the results set. - Creating temporary tables to store the results of a complex query which …