Best Practices For Managing And Analyzing Geospatially Referenced Data

Understanding Geospatial Data

Geospatial data refers to information about geographic locations that are represented by numerical coordinate values. The coordinates pinpoint positions on the Earth’s surface and define the geospatial component of the data. Understanding key properties like coordinate systems, map projections, and metadata provides the foundation for working with geospatial data effectively.

Defining Key Properties: Coordinates, Projection, Metadata

The coordinate values in geospatial data locate geographic features relative to the surface of the Earth. The most common coordinate system represents longitude and latitude values. Additional properties like height or depth may be included for 3-dimensional positioning. The coordinate system and map projection contextualize the coordinates and enable accurate real-world measurements and analysis.

Metadata provides descriptive information about the geospatial data source and contents. This may encompass details on accuracy, precision, scale, lineage, positional uncertainty, and other relevant attributes. Thorough metadata aids in data discovery and assessing fitness for use.

Common Formats: Vector vs Raster

Geospatial data formats encode locations and attributes of geographic features digitally. Key formats include vectors and rasters.

Vectors represent coordinates and related data using discrete point, line, or polygon geometries. Common vector formats include Shapefile, GeoJSON, KML, and others. Vectors efficiently store features and enable advanced spatial analysis methods.

Rasters encode data in a grid of cells over a geographic area. The values in the grid cells depict a continuous surface or imagery. Some examples are digital elevation models, aerial/satellite imagery, and climate data. Rasters efficiently model continuous phenomena.

Example Code for Reading a GeoJSON File

GeoJSON is a popular open standard for encoding geographic data using JavaScript Object Notation (JSON). Here is example Python code for reading a GeoJSON file and accessing the coordinate values:

import json
import geopandas 

with open("file.geojson") as f:
  data = json.load(f)

gdf = geopandas.GeoDataFrame.from_features(data)

# Print first coordinate pair 
print(gdf.geometry[0].coords[0])

Cleaning and Preparing Geospatial Data

Real-world geospatial data often requires preprocessing before analysis. This may involve handling missing coordinates, transforming projections, enriching metadata, and more. Applying consistent, standardized practices improves data quality.

Handling Invalid or Missing Coordinate Values

Raw geospatial data can contain invalid out-of-range coordinates, null values, or gaps. Identifying and addressing issues follows:

Check for null coordinates and remove or interpolate missing values
Flag outliers and handle per application requirements
Project data to consistent coordinate system if needed

Choosing the Appropriate Projection

Map projections transform the 3D Earth to a 2D plane for display and measurement. Certain projections are optimized for specific regions or use cases. Best practices for projections involve:

Select projections suitable to dataset coverage area
Minimize distortion over the zone of interest
Document project parameters fully in metadata

Enriching with Accurate Metadata

Comprehensive metadata greatly augments the value of geospatial data assets over time. Metadata best practices include:

Fully detail data lineage, processing workflows, quality control steps
Provide positional accuracy estimates for all coordinate data
Identify appropriate usage, constraints, and lifecycle status

Analyzing Spatial Relationships

GIS provides powerful methods for modeling spatial relationships and interactions between geographic features. This enables key spatial analysis techniques.

Calculating Distances and Proximities

Determining distances or proximities between features aids many analyses like identifying access disparities for facilities. Spatial analysis approaches include:

Buffer polygons around features and find overlaps
Use geodetic measurements for precision over large areas
Apply network analysis to incorporate real-world paths

Identifying Spatial Clusters and Outliers

Detecting geospatial clusters and outliers reveals interesting patterns in data. To implement:

Aggregate data into hexbins or grids to identify hotspots
Calculate statistics like Moran’s I to quantify spatial autocorrelation
Use Anselin Local Moran’s I to pinpoint outlier locations

Example Code for Buffer Analysis

Buffers create polygons enclosing areas within specified distances of geographic features. This assists proximity analysis. Below uses GeoPandas to buffer points by 500 meters:

import geopandas

df = geopandas.read_file("points.geojson") 

# Generate 500m buffers  
buffers = df.buffer(500)

# Write buffers to file
buffers.to_file("buffers.geojson")

Visualizing Patterns and Trends

Maps and spatial visualizations clearly convey geospatial datasets to users. Effective representations distill key information and relationships.

Static vs Interactive Maps

Static maps present a snapshot for printing or publishing online. Interactive web maps enable users to explore data dynamically. Guidelines include:

Use static maps to highlight analysis results and conclusions
Develop interactive maps to enable in-depth data exploration
Provide contextual basemaps and minimize visual clutter

Color Schemes for Choropleth Maps

Choropleth maps use color gradients to encode data values over regions. Ensuring accessible and understandable color schemes involves:

Use proper data classification methods
Design color progressions perceptually optimized
Allow customization of color schemes where possible

Creating Inset Maps to Highlight Regions

Inset maps frame details views of smaller areas within a larger basemap. Inset maps effectively:

Provide geographic context for detailed view
Draw attention to key areas of interest
Maintain consistent scale/projection between maps

Geospatial Data Storage and Management

Managing and providing access to geospatial data brings additional considerations versus other data types regarding storage, platforms, and reproducibility.

Spatial Databases Like PostGIS

Spatial database systems like PostGIS enable advanced GIS data storage, indexing, and processing:

Optimize for complex geospatial queries
Efficiently handle very large vector/raster datasets
IntegrateSpatial DBMS with other systems via SQL access

Cloud-Based Geospatial Platforms

Cloud platforms provide scalable geospatial data sharing and analysis:

Facilitate centralized data management and access
Offer turn-key deployment of web GIS applications
Integrate with leading commercial/open-source GIS tools

Version Control for Reproducibility

Version control systems help track geospatial datachanges and enable reproducibility:

Log all revisions to geospatial datasets
Tag versions used in analyses/outputs
Compare differences across versions