Working as a Data Analyst has taught me many things about working with all kinds of data.

Image credit: https://pixabay.com/illustrations/matrix-full-program-data-code-3145364/

It’s opened my eyes to working with geospatial data, Statistics Canada data, and research data. Of these, I had the most familiarity with geospatial data when first coming into this position back in 2014; but even so I was exposed to so many more ways of using this kind of data than I had previously experienced. See the following three sections to get a sense for what I’ve learned and worked on over the past few years.

Geospatial Data

Geospatial data can be defined as follows (Esri, n.d.):

  1. “Information about the locations and shapes of geographic features and the relationship between them, usually stored as coordinates and topology.”
  2. “Any data that can be mapped.”

This includes data that may be nonspatial in nature (e.g. tables), but that can have a bearing on spatial analysis. Geospatial data comes in two main types: vector and raster data.

Vector data

Vector data are represented by one of three geometries: points, lines, or polygons. These are also associated with a linked database (table), where each feature (e.g. point) corresponds to a record in the database. This data is often used to represent discrete geography, such as cities, roads, rivers, boundaries, etc.

To learn more about vector data, see this:

Raster data

Raster data are represented as pixels in a grid, where all pixels are equal in size. The size of the pixels determines the resolution of the raster dataset, e.g. a 30-metre pixel means that the data is at a resolution of 30m. This is very similar to an image, and rasters can be represented as satellite imagery. However, they can also be images showing different types of data and are usually best used to represent continuous data (i.e. data that gradually change over space and time). Examples of continuous data would be temperature, precipitation, population density, etc.

To learn more about raster data, see this workshop I created and delivered as Data Analyst:

Statistics Canada Data

Working at the uOttawa Library really opened my eyes to the wealth of data available to all of us through Statistics Canada data. This includes survey data, census data, and various other products. data can come in many different formats and at different levels of confidentiality. The more confidential the data, the harder it is to get.

Research Data

Research data is data that have been produced as a result of ongoing research, often in academia. The storage, management, discoverability, and potential reuse of this data relates to Research Data Management (RDM) and is a topic of much conversation. To read more about my thoughts on RDM and making data more open, please see the Information for all under my Professional Values.  

The following documents highlight some of my work relating to research data: