Take-home Exercise 1: Application of Spatial Point Patterns Analysis to discover the geographical distribution of functional and non-function water points in Osun State, Nigeria

Published

January 27, 2023

Modified

April 16, 2023

Note

This handout provides the context, the task, the expectation and the grading criteria of Take-home Exercise 1. Students must review and understand them before getting started with the take-home exercise.

Setting the Scene

Water is an important resource to mankind. Clean and accessible water is critical to human health. It provides a healthy environment, a sustainable economy, reduces poverty and ensures peace and security. Yet over 40% of the global population does not have access to sufficient clean water. By 2025, 1.8 billion people will be living in countries or regions with absolute water scarcity, according to UN-Water. The lack of water poses a major threat to several sectors, including food security. Agriculture uses about 70% of the world’s accessible freshwater.

Developing countries are most affected by water shortages and poor water quality. Up to 80% of illnesses in the developing world are linked to inadequate water and sanitation. Despite technological advancement, providing clean water to the rural community is still a major development issues in many countries globally, especially countries in the Africa continent.

To address the issue of providing clean and sustainable water supply to the rural community, a global Water Point Data Exchange (WPdx) project has been initiated. The main aim of this initiative is to collect water point related data from rural areas at the water point or small water scheme level and share the data via WPdx Data Repository, a cloud-based data library. What is so special of this project is that data are collected based on WPDx Data Standard.

Objectives

Geospatial analytics hold tremendous potential to address complex problems facing society. In this study, you are tasked to apply appropriate spatial point patterns analysis methods to discover the geographical distribution of functional and non-function water points and their co-locations if any in Osun State, Nigeria.

The Task

The specific tasks of this take-home exercise are as follows:

Exploratory Spatial Data Analysis (ESDA)

  • Derive kernel density maps of functional and non-functional water points. Using appropriate tmap functions,
  • Display the kernel density maps on openstreetmap of Osub State, Nigeria.
  • Describe the spatial patterns revealed by the kernel density maps. Highlight the advantage of kernel density map over point map.

Second-order Spatial Point Patterns Analysis

With reference to the spatial point patterns observed in ESDA:

  • Formulate the null hypothesis and alternative hypothesis and select the confidence level.
  • Perform the test by using appropriate Second order spatial point patterns analysis technique.
  • With reference to the analysis results, draw statistical conclusions.

Spatial Correlation Analysis

In this section, you are required to confirm statistically if the spatial distribution of functional and non-functional water points are independent from each other.

  • Formulate the null hypothesis and alternative hypothesis and select the confidence level.
  • Perform the test by using appropriate Second order spatial point patterns analysis technique.
  • With reference to the analysis results, draw statistical conclusions.

The Data

Apstial data

For the purpose of this assignment, data from WPdx Global Data Repositories will be used. There are two versions of the data. They are: WPdx-Basic and WPdx+. You are required to use WPdx+ data set.

Geospatial data

This study will focus of Osun State, Nigeria. The state boundary GIS data of Nigeria can be downloaded either from The Humanitarian Data Exchange portal or geoBoundaries.

Grading Criteria

This exercise will be graded by using the following criteria:

  • Geospatial Data Wrangling (20 marks): This is an important aspect of geospatial analytics. You will be assessed on your ability to employ appropriate R functions from various R packages specifically designed for modern data science such as readxl, tidyverse (tidyr, dplyr, ggplot2), sf just to mention a few of them, to perform the entire geospatial data wrangling processes, including. This is not limited to data import, data extraction, data cleaning and data transformation. Besides assessing your ability to use the R functions, this criterion also includes your ability to clean and derive appropriate variables to meet the analysis need. (Warning: All data are like vast grassland full of land mines. Your job is to clear those mines and not to step on them).

  • Geospatial Analysis (30 marks): In this exercise, you are expected to use the appropriate thematic and analytics mapping techniques and R functions introduced in class to analysis the geospatial data prepared. You will be assessed on your ability to derive analytical maps by using appropriate rate mapping techniques.

  • Geovisualisation (20 marks): In this section, you will be assessed on your ability to communicate the complex spatial statistics results in business friendly visual representations. This course is geospatial centric, hence, it is important for you to demonstrate your competency in using appropriate geovisualisation techniques to reveal and communicate the findings of your analysis.

  • Reproducibility (20 marks): This is an important learning outcome of this exercise. You will be assessed on your ability to provide a comprehensive documentation of the analysis procedures in the form of code chunks of RMarkdown. It is important to note that it is not enough by merely providing the code chunk without any explanation on the purpose and R function(s) used.

  • Bonus (10 marks): Demonstrate your ability to employ methods beyond what you had learned in class to gain insights from the data. The methods used must be geospatial in nature.

Submission Instructions

  • The write-up of the take-home exercise must be in Quarto html document format. You are required to publish the write-up on Netlify.
  • The R project of the take-home exercise must be pushed onto your Github repository.
  • You are required to provide the links to Netlify service of the take-home exercise write-up and github repository on eLearn.

Due Date

12th February 2023 (Sunday), 11.59pm (midnight).

Learning from senior

You are advised to review these sample submissions prepared by your seniors.

Q & A

Please submit your questions or queries related to this take-home exercise on Piazza.

Peer Learning