My first article on ArcGIS Maps for Power BI was written in June 2017. It’s time for a review since the technology and my proficiency have both improved. The examples use data on mobile money agents in Kenya and help us to assess how Power BI can be used for data-driven storytelling.
Recently I took the course Visualizing Geospatial Data in Python on DataCamp’s interactive learning platform. To consolidate the new learning, I visualized some spatial datasets for Kenya. Let us find out how the location of Financial Service Providers relates to population.
Under the enactment of Kenya’s 2010 constitution, 47 counties were created as part of a devolved government. For benchmarking purposes, segmentation of these counties should be based on similarity. Clustering models and machine learning allow us to do just that.
Anyone loves a good story, even more so when it is true. This explains the quest for data-driven visual storytelling in all spheres of life across the world. Could it help me as a Geospatial Data Scientist to reach a wider audience and make a bigger impact? Let us find out.
In May 2018, I received my Microsoft Professional Program Data Science Certificate. I thoroughly enjoyed going through the different units and now look forward to putting my new-found skills as a data scientist into practice. Allow me to share my experience, especially with those who want to follow my footsteps.
In two earlier articles on Power BI I showed how to connect to Google Analytics and how to create an ArcGIS Map visualization. I have now gone through the course Analyzing and Visualizing Data with Power BI, and will give you access to some of the interactive reports that I have built.
I just completed Essential Statistics for Data Analysis using Excel and I realized that statistics can be very enjoyable. Especially when you develop hands-on skills and are given practical examples with interesting data and insights. It inspired me to write this article on descriptive statistics, which uses some of Kenya’s open datasets. Have fun!
About 70% of Nairobi’s population uses matatus and buses to commute between home and work. Despite this, information on matatu/bus routes and stops remained scarce until the Digital Matatus project collected the field data in 2014. Let’s take a closer look at their GIS data and see whether we can put it to good use.
Kenya conducted its last population and housing census in 2009. Total population was initially estimated at 38.6 million, but this has now been revised to 37.7 million. Since population is a key factor in planning, let us review the census data with the aim of enhancing its dissemination and utilization.
Statement or question? Data science is in the news as a discipline that can solve business problems through big data. The fast-growing geospatial industry has been handling large volumes of spatial data for decades. Let’s look at both and find out how they can sustain each other’s growth.