Introduction to GIS for Public Health

Introduction

Introduction

  • Public health - demands a broader view!
  • The need for participatory decision making in public health
  • The transparency of open data science approach

The third upcoming area of research methods

  • Observational
  • Experimental
  • Computational/data science/ML/AI, etc.

What information does GIS use?

  • Data that defines geographical features like roads, rivers
  • Soil types, land use, elevation
  • Demographics, socioeconomic attributes
  • Environmental, climate, air-quality
  • Annotations that label features and places

What is (Geo)-Spatial Data Science?

  • Analyse and extract insights from geospatial data
  • Work with real-world data on a number of domains and problems
  • Acquire key data science skills and important tools to answer spatial questions

A valuable tool for public health policy advocacy

The Process of Geospatial Data Science

Mininum skills for public health data science

Hard Skills - Programming Language - Transparency and Reproducibility

Soft Skills - Communication - Storytelling - Geospatial analytics - Ethical skills

Tools for Spatial Data Science (SDS)

Graphical User Interfaces (GUIs)

  • QGIS and GRASS has revolutionized Open source Geographic Information Systems (GIS).
  • However, the reproducibility aspect has many challenges

Command Line Interfaces (CLIs)

  • R and
  • Python

are a good way to bring in reproducible algorithms for GIS/SDS

The Geodata ‘revolution’

Advanced Hardware

  • High-performance computer hardware
  • Efficient algorithms to process vast data sets

Scalable Software

  • Scalable solutions with the R
  • extract valuable insights from the noise

Spatial Databases

  • The advent of spatial databases

Healthcare Data

Traditionally data in healthcare are:

  • Collected for the purpose (carefully designed)
  • Detailed and informative (“rich profile and portraits”)
  • High quality

However, they are:

  • Massive enterprises (very costly)
  • Coarse resolution (need to be aggregated to protect privacy)
  • Slow - the more detailed, the less frequent they are available

New Forms of Spatial Data

  • Tied into the Geodata revolution

  • Accidental: created for different purposes but available for analysis as a side product

  • Diverse: resolution and quality but, potentially much more detailed in both space and time

Challenges (Arribas-Bel, 2014)

  • Bias
  • Technical barriers
  • Methodological “mismatch”

(Geo)-Spatial Visualization

Spatial Visualization

By encoding information visually, they allow to present large amounts of numbers in a meaningful way.

A Map for Everyone

A Real Public Health Tool

  • Maps can fulfill several needs, looking very different depending on the end-goal.

Three Main Dimensions

  • Knowledge of what is being plotted
  • Target audience
  • Degree of interactivity

MacEachren & Kraak (1997)

MacEachren & Kraak (1997)

DiBiase’s (1990) “Swoopy”

Translating numbers into a (visual) language that the human brain “speaks better”

Exploratory Visualization


> “forces us to notice what we never expected to see”

(Tukey 1977: vi)


  • Mostly for researchers in the course of the research process.

  • Many, quick and dirty, and rather unattractive graphs.

Explanatory Visualization


> “forces readers to see the information the designer wanted to convey”

(Kosslyn 1994: 271)


  • Mostly for others after the research is completed.

  • Few, carefully crafted, and attractive graphs.

Choropleths

Thematic map in which values of a variable are encoded using a colour gradient of some sort

  • Counterpart of the histogram


Both allows us to guage the distribution of a variable.

Spatial Weights

Spatial Weights

For a statistical method to be explicitly spatial, it needs to contain some representation of the geography, or spatial context.

(Geo)-Spatial Visualization:

  • translating numbers into a (visual) language (colors) that the human brain can interpret.

Spatial Weights Matrices:

  • translating geography into a (numerical) language that a computer can interpret.

Spatial Weight Matrices

Spatial Weights Matrices are building block for spatial analysis and statistics.

  • They are used to assign a weighted average or sum of neighbouring data values to an observation, or other point in space.

  • Relates to concepts of spatial ‘smoothing’ and interpolating data

Applications of Spatial weights

Spatial weights form the core element in several spatial analysis techniques

  • Spatial autocorrelation
  • Spatial clustering/geo-demographics
  • Spatial regression

Spatial Heterogeneity

Most influential local determinants of household energy expenditure

Mashhoodi et al., 2019 (doi:10.1080/19475683.2018.1557253)

REDCAP and MLR

Late-stage breast cancer rates in Chicago region in 2000: (a) ZIP code areas, (b) REDCAP-constructed areas

::: aside Wang et al., 2015 (doi:10.1080/19475683.2019.1702099) :::

2SFCA

  • Two-step floating catchment area (2SFCA) method
  • Hospital potential crowdedness

Wang et al., 2018 (10.1080/19475683.2019.1702099)

Recap