Introduction to Spatial Data Science

Data Science

• Spatial Data Science is a multidisciplinary field that uses geographic principles and data to analyze, understand, and interpret spatial information
• Data is any information that can be collected and analyzed.
• Qualitative Data is descriptive, non-numerical, and deals with characteristics, qualities, or attributes. Examples include names, colors, textures, and descriptions.
• Quantitative Data is numerical, represents quantities or measurements, and can be further divided into:
  • Discrete Data: has distinct values, like income classes
  • Continuous Data: has measurable values, like temperature
• Data Science is a multidisciplinary field that encompasses several stages:
  • Data Collection and Preparation
  • Data Analysis and Exploration
  • Data Visualization
  • Machine Learning and Predictive Modeling
  • Big Data Analytics
• Spatial Data Science Process involves five steps:
  • Data Collection
  • Data Engineering and Integration
  • Modeling and Scripting
  • Analytics
  • Visualization, Storytelling, and Sharing

Geodesign Framework

• Geodesign is conceived as an iterative design method that uses stakeholder input, geospatial modeling, impact simulations, and real-time feedback to facilitate holistic designs and smart decisions.
• GeoDesign problems typically involve six key questions
  • How should the context be described? (Data)
  • How does the context function? (Knowledge)
  • Is the context working well? (Values)
  • How might the context be altered? (Data)
  • What differences might the changes cause? (Knowledge)
  • How should the context be changed? (Values)

Shapefiles

• Shapefiles are a geospatial data format used for storing geographic features like points, lines, and polygons.
• Mandatory files for shapefiles include:
  • .shp: The main data file
  • .shx: Index file to speed up data access
  • .dbf: Attribute table for storing non-spatial data
• Optional files for shapefiles include:
  • .sbn and .sbx: Spatial index files to optimize spatial queries for faster loading times.
  • .prj: Projection file to define the coordinate system
  • .cpg: Code page file to describe the encoding applied to the shapefile.

American Community Survey (ACS)

• ACS provides local statistics on critical planning topics such as age, children, commuting, education, and employment.
• Data collection:
  • A household sample of 3.5 million addresses is surveyed each year.
  • Data is collected through the Internet, mail, and in-person visits.
  • Data collection for each monthly panel takes place over a three-month period.
• Data release:
  • Data is typically released one year after it is collected.
  • Supplemental estimates are simplified versions of the ACS tables.
• Frequency: The ACS is conducted every month and the data are released every year.

National Historical Geographic Information System (NHGIS)

• NHGIS provides easy access to summary tables and time series of population, housing, agriculture, and economic data, along with GIS-compatible mapping files.
• Coverage: Data is available from 1790 to the present for all levels of U.S. geography.
• Important Note: Geographic boundaries sometimes change while GEOIDs remain the same. Make sure you are comparing comparable data across time periods.

Data.census.gov

• Data.census.gov is the Census Bureau's primary tool for accessing data from the American Community Survey (ACS), the decennial census, and other Census Bureau data sets.
• My Congressional District and Census Business Builder are specialized tools that provide users with quick and easy access to statistics.

TIGER Data and Products

• TIGER products are spatial extracts from the Census Bureau's Master Address File (MAF)/TIGER database (MTDB) and are designed for use with GIS software.
• Data content: TIGER products include features like roads, railroads, rivers, and legal/statistical geographic areas.
• TIGER products:
  • TIGERweb: A web-based system that allows users to visualize TIGER data online or stream it to mapping applications.
  • TIGER/Line with Selected Demographic and Economic Data: Geodatabases (or shapefiles for some 2010 Census data) joined with selected attributes from the census and ACS.
  • TIGER/Line Shapefiles: Provide legal boundaries, roads, address ranges, water features, and more for linking to demographic data using GEOID.
  • TIGER/Line Geodatabases: Spatial extracts from the Census Bureau's MTDB.

Data Visualization Best Practices

• Geospatial data requires specialized chart types like maps and stacked bar charts.
• Maps are often best when paired with other charts to give more information.
• Emphasize important data: Put the most important variables on the X and Y axes. Less important data can be represented with color, size, or shape.
• Legibility: Rotate views to fit long labels when necessary.
• Organization: Use bullet charts to visually compare actual and target numbers.
• Avoid overloading: Break down views into smaller multiples, limit colors and shapes, and use interactive views only when necessary.

Data Governance

• Data Governance Literacy is about training and upskilling key roles in data governance.
• Four Pillars of Data Governance:
  • Increasing data usage
  • Improving data quality
  • Identifying data lineage
  • Ensuring data protection
• Modern approach to data governance: Focus on increasing data usage to drive support for data governance initiatives and gain value from data assets.
• Data stewards:
  • Existing data stewards can provide insights on skills needed.
  • New data stewards can highlight their learning needs for success.
  • Data owners offer valuable perspectives on success.
• Data Governance Literacy Program:
  • Start a backlog to track content for training.
  • Develop a checklist for desired content.
  • Break down work into smaller efforts and review with stakeholders.
  • Prioritize the backlog.
• Disrupting Data Governance:
  • Increasing usage: Drive support for data governance by demonstrating value and increased utilization.
  • Improving quality: Boost trust by focusing on data quality efforts, as most organizations struggle with this.

Explore the fundamentals of Spatial Data Science, a multidisciplinary field focused on understanding spatial information through geographic principles. This quiz covers various data types, the data science process, and the unique aspects of qualitative and quantitative data. Test your knowledge and enhance your understanding of this vital field.