Week 6.2

GEOGRAPHY 280: THINKING SPATIALLY IN A DIGITAL WORLD

  • Course Code: GEOG 280 L01 - (Fall 2025)

  • University of Calgary

BASIC DATA QUALITY CHECKING (QC)

  • Introduction to open data and project introductions (OE3).

RECAP OF TOPICS COVERED

  • Summary of key processes:

    • Travel areas

    • Finding nearest locations

    • Creation of centroids

  • Discussion on ESRI credit management and data filtering.

LATITUDE/LONGITUDE PRECISION

Importance of Coordinate Precision

  • Coordinates can indicate various spatial details:

    • 28°N, 80°W indicates a general area.

    • 28.5°N, 80.6°W specifies a city.

    • 28.52°N, 80.68°W points to a neighborhood.

    • 28.523°N, 80.683°W refers to a suburban cul-de-sac.

    • 28.5234°N, 80.6830°W indicates a particular corner of a house.

    • 28.5234571°N, 80.6830941°W implies targeting an exact room location or specific point.

Excessive Precision Consequences

  • Going beyond practical precision can lead to pointlessness:

    • E.g., coordinates indicating individual atoms are impractical and unnecessary.

  • Reference: XKCD comic regarding extreme level of precision.

DATA QUALITY CHECKING

Analysis Considerations

  • Requirements for effective analysis:

    • Features in specific shapes (point, line, polygon).

    • No blanks or zeros.

    • Minimum record limits for processing.

    • Error-free topology, which requires nearby roads for route measurements.

CENTROID ANALYSIS

Input Features

  • Feature selection for centroids:

    • Users must specify if centroids should fall within features or at true geometric centroids.

Example Data

  • Input layer:

    • Census districts - Count of features: 306

  • Output requirements:

    • Result layer naming and saving protocols.

IMPORTING CSV FILES INTO ARCGIS ONLINE

  • CSV: Comma-Separated Values format.

    • Opening in a text file shows commas separating each attribute;

    • In Excel, the data presents as a table.

  • Requirements for importing:

    • Inclusion of latitude and longitude columns.

    • Attribute headers must have no spaces or special characters except underscores.

  • Importance of cleaning data before upload:

    • Avoid extra spaces and ensure concise names.

TIPS IF ANALYSIS DOES NOT WORK

Steps for Troubleshooting:

  1. Verify input layers.

  2. Ensure data model conformity (vector vs raster).

  3. Confirm correct shapes (point, line, polygon).

  4. Check for blanks/zeroes.

  5. Review documentation for analysis limitations (e.g., minimum/maximum data points).

  6. Experiment with different parameters.

  7. Attempt re-adding layers.

  8. Log out and back into ArcGIS Online and consult online resources.

VIEWING ATTRIBUTE DATA

Steps for Evaluating Data:

  • Read the metadata for understanding column meanings and data collection.

  • Check data units.

  • Sort columns to identify outliers or anomalies.

  • Identify any blanks or null values.

UNDERSTANDING NULLS, ZEROS, AND PLACEHOLDER NUMBERS

Definitions:

  • Null Values: Indicate missing data, not zeroes. For instance, in temperature data, a measurement might be entered as null if missed.

  • Zero Values: Represent the absence of something measurable, e.g., zero animal crossings means none occurred.

  • Placeholder Numbers: Used when software requires numeric input; often meant to indicate missing values (like -9999).

Potential Analysis Errors:

  • Nulls and zeros can lead to different analysis outcomes:

    • May skip records or cause analyses to not run.

    • Can be misrepresented if not clearly understood.

IMPORTANCE OF ATTRIBUTE SELECTION

  • Quality datasets are crucial. Typos or format differences complicate analysis.

  • Example variations in names stored in databases:

    • Different forms of the name “Timothy Hunt” leads to complications in filtering when compared to standardized forms.

MEASUREMENT TOOL USAGE

  • Utilize the Measurement tool on maps to confirm distance/area-related analyses.

    • Ask if result validity makes intuitive sense.

MIXING AND AGGREGATING DATASETS

  • Be cautious when merging datasets from diverse sources:

    • Cohesion is better maintained in a singular governmental dataset than when combining multiple provincial datasets.

    • Differing standards and collection methods of multiple contributors can affect final results.

SYLLABUS AND COURSE OUTCOMES

Course Learning Outcomes:

  1. Understanding how maps represent spaces.

  2. Describing knowledge production through geospatial technologies.

  3. Recognizing applications of geospatial technologies.

  4. Applying industry-standard software for mapping tasks.

  5. Generating an applied mapping project.

YOUR GEOSPATIAL PROJECT

OE/ICE 3 Project Guidelines:

  • Initial proposal using Open Calgary and Living Atlas data.

  • Components include:

    • Title and research question.

    • Dataset table with source information & relevance.

    • Importance paragraph for research question.

    • Analysis approach paragraph for geospatial data.

  • Linkages to Tutorials 4 and 5 facilitate progressive learning.

OUTCOMES FROM THE PROJECT:

  • Skills gained:

    • Writing research proposals

    • Working with open data

    • Data quality checking

    • Project management

    • Geospatial analysis practice.

    • Resulting in a public-facing Storymap to showcase work.

FINDING PROJECT DATA

  • Required steps:

    • Locate data, evaluate data quality, and analyze data.

  • Repetition improves skill in navigating data challenges.

OPEN DATA EXPLANATION

Definition:

  • Open data can be used and modified freely without restrictions. Typically sourced from government entities, public institutions, and select non-profits.

  • Example search strategy: “Open data Calgary.”

METADATA IMPORTANCE

  • Metadata: Data regarding the data structure, source, update frequency, and relationships to other datasets, enhancing understanding.

  • Example usage: Government of Canada's metadata portals for clarifying dataset particulars.

GOVERNMENT OPEN DATA SOURCES

  • Resources:

    • Government of Canada - Open Government

    • Government of Alberta - Open Government Program

    • City of Calgary - Open Data initiatives.

ADDITIONAL DATA SOURCES

Open Street Map

  • Community-created map data that can be freely used when credited.

  • Methodologies involve local knowledge and GPS technology.

DATA ACCESS CHALLENGES

  • Potential for large data volumes that may vary in collection quality and completeness.

  • Consideration needed for the variance between governmental and stakeholder-reported datasets.

DATA IN THE PUBLIC DOMAIN

  • Comprises information available online which can vary in quality. Access can occur via transcription, downloading, or web scraping.