OpenStreetMap Part 1: Leveraging open source data for development work

Introduction Open-source tools for development work allows for the power of big data, machine learning and AI to come together to benefit people who may need it most. Open source tools are those with publicly available source code that can be downloaded or changed entirely free of charge. There is a wide range of open-source tools available these days: for instance, this blog is entirely written in the R programming language, free and open source.

US 2020 Presidental Election and Rural - Urban Divide

Introduction The US Presidential vote count has nearly finished. Trump is fighting the results in court though appears to have lost, but a clear theme that’s coming out is the contrast of rural and urban voters. The theory seems to go: Trump voters for the most part come from rural areas, and the election is based on a rural vs urban debate. We can test this theory using some data science approaches - mainly, cleaning and merging data from several places into a useful format.

U.S. Food Deserts

Introduction A considerable issue today related to food and rural population research are food deserts. Food deserts are a complicated issue, but the idea centres on a simple premise: areas, where it’s hard to reach a grocery store or access food, can be thought of as food deserts. If you’re interested in knowing more about the discourse on food deserts, I’d recommend looking into these papers:

Be like me - looping through shortest distance analysis

Introduction I’ve been doing some work lately on social networks that exist between organisations or institutions. This is nice as it builds on some of my dissertation work, and I generally find it quite interesting. Networks that form between organisations are often quite powerful, in that they can illustrate where strong areas of like-minded work exist or where new connections might be useful in strengthing one organisation’s influence. Why like me?

Covid-19 and Rural Areas in the U.S.

First published on 26-April-2020 Last updated on 16-Aug-2020 Introduction Now that we are square in the middle of the Covid-19 pandemic, I thought it might be beneficial to look at some statistics associated with the number of cases. We’ll differentiate our analysis by focusing on cases of Covid-19 in rural areas of the U.S. There are a couple of reasons for this: mainly, rural analytics is my speciality, so while I don’t know much about the virus, I do know some about rural societies and economies; we can easily find pertinent data on rural counties; and, we can utilise some cool built-in R functions to help us along the way.

Networks from survey data: Creating mock data

Why create a new dataset? I’d like to do a series of posts looking at social network analysis using primary data (i.e. data collected by yourself.). There are a lot of different examples of when you might want to use a survey to collect data for use in analysing social networks. But that’s for another time. The purpose of this post is to create a new dataset that can be used in practising social network analysis in future posts.

Apples for apples I

Introduction This is the initial Deltanomics blog post. So, in this post, I’ll cover a few different approaches to analysis and data visualisation rather quickly that provides a good overview of the types of things covered in this blog. Let’s start with loading the packages we’ll use. Also, let’s create a ggplot theme that allows us to easily make changes when we want. ## Libraries used in analysis library(tidyverse) library(magrittr) library(scales) library(RColorBrewer) library(janitor) library(ggraph) library(tidygraph) library(graphlayouts) library(flextable) ## a congruent theme throughout for plots post_theme <- function(.