GIS and Spatial Data Management

SOP-L804 Revision 0.1
Author

Marc Los Huertos

EA Program, Pomona College

Published

2026-06-26

1 Purpose

This SOP establishes procedures for acquiring, organizing, projecting, and archiving geospatial data used in EA Program research. It covers coordinate reference systems, file formats, metadata standards, and reproducible mapping workflows in R, ensuring that spatial data are interoperable across projects and analysts (Lovelace et al., 2019; Pebesma & Bivand, 2023).

2 Scope

  • Applies to: All vector (point, line, polygon) and raster (satellite imagery, DEMs, interpolated surfaces) data used in EA Program projects
  • Does not apply to: Drone image acquisition or photogrammetric processing (see SOP-L805); GPS receiver operation in the field (see relevant field SOPs)
  • Location: EA Program laboratory (SGM 133) and RStudio Server

3 Responsibilities

Role Responsibility
Lab PI Approve spatial data management plan; ensure data-sharing compliance
GIS Lead Maintain spatial data library; enforce projection and metadata standards
Analyst / Student Follow naming conventions; document coordinate systems; archive final layers
Data Reviewer Verify spatial metadata completeness before publication

4 Definitions

  • Coordinate reference system (CRS): A framework that associates coordinates with locations on Earth; consists of a datum (reference ellipsoid), a projection (for 2D maps), and a coordinate system (e.g., latitude/longitude or meters); data layers must share the same CRS for spatial operations.
  • Geodatabase: A structured container for storing multiple spatial datasets, attribute tables, and topological rules in a single file; the preferred format for complex GIS projects with many related layers.
  • GeoTIFF: A TIFF image file with embedded geospatial metadata (CRS, extent, resolution); the standard raster format for spatial data exchange in remote sensing and GIS.
  • Raster: A spatial data model representing continuous surfaces as a grid of equally sized cells (pixels), each with one or more attribute values; used for elevation, imagery, and interpolated surfaces.
  • Shapefile (.shp): A common vector spatial data format introduced by Esri; stores geometry (points, lines, polygons) and attribute data in multiple associated files (.shp, .dbf, .shx, .prj minimum); widely supported but limited by 2 GB file size and 10-character field name constraints.
  • Vector: A spatial data model representing discrete features as points, lines, or polygons defined by coordinate pairs; used for sample locations, stream networks, and land parcel boundaries.
  • WGS84 (World Geodetic System 1984): The global geodetic datum used by GPS; EPSG code 4326; the required CRS for all EA Program GPS-collected point data before projection into a local coordinate system.

5 Safety and Hazards

GIS work is a computational activity with no physical hazards. When using GPS receivers or conducting ground-truth fieldwork, follow SOP-F001 Field Safety.

6 Equipment and Materials

6.1 Software

Item Access Notes
R (version 4.x) RStudio Server Primary analysis platform
sf package CRAN Vector data handling
terra package CRAN Raster data handling
tmap / ggplot2 CRAN Map production
QGIS (3.x) Free download Visual inspection and digitizing
Google Earth Engine earthengine.google.com Satellite imagery processing

6.2 Hardware

Item Location Notes
GPS receivers (Garmin) SGM 133 equipment cabinet Field coordinate collection
High-resolution monitor SGM 133 For digitizing and map review

7 Procedure

7.1 Coordinate Reference Systems

All EA Program spatial data must use one of the following coordinate reference systems (CRS). Document the CRS in every file, script, and metadata record.

CRS Name EPSG Code Use Case
WGS 84 (geographic) 4326 GPS data, web maps, data exchange
NAD83 / UTM Zone 11N 26911 Local analysis in Southern California
NAD83 / California Zone 5 2229 County and municipal data
TipTip

When in doubt, store data in EPSG:4326 (latitude/longitude, WGS 84). Transform to a projected CRS for distance and area calculations.

When loading spatial data in R, always verify and set the CRS explicitly:

Code 
library(sf)

# Read a shapefile
sites <- st_read("data/spatial/sampling_sites.shp")

# Check CRS
st_crs(sites)

# Transform to UTM Zone 11N for area calculations
sites_utm <- st_transform(sites, crs = 26911)

7.2 File Formats

Format Extension Use Case Notes
GeoPackage .gpkg Preferred for all vector data Single file, open standard
Shapefile .shp + .dbf + .shx + .prj Legacy compatibility Keep all component files together
GeoTIFF .tif Raster data Include CRS in header
CSV with XY .csv Simple point data Requires separate CRS documentation
GeoJSON .geojson Web applications Always WGS 84

GeoPackage (.gpkg) is the preferred format for new projects because it stores geometry, attributes, and CRS in a single file without the limitations of shapefiles (10-character field names, 2 GB size limit) (Pebesma & Bivand, 2023).

7.3 File Naming and Directory Structure

Follow the conventions in SOP-L803 with the addition of a data/spatial/ subdirectory:

project_name/
├── data/
│   ├── raw/
│   ├── processed/
│   ├── spatial/
│   │   ├── raw/          # Downloaded or collected spatial data
│   │   ├── processed/    # Clipped, projected, joined layers
│   │   └── metadata/     # FGDC or ISO metadata files
│   └── metadata/
├── analysis/
│   ├── scripts/
│   └── maps/             # Exported map figures
└── README.md

7.4 Spatial Data Acquisition

Common data sources for EA Program research:

Source URL Data Types
USGS National Map nationalmap.gov DEMs, hydrography, boundaries
USGS EarthExplorer earthexplorer.usgs.gov Landsat, NAIP imagery
CA State Geoportal gis.data.ca.gov State and county layers
NRCS Web Soil Survey websoilsurvey.sc.egov.usda.gov SSURGO soil data
OpenStreetMap openstreetmap.org Roads, buildings, land use
Google Earth Engine earthengine.google.com Multi-temporal satellite data

When downloading external data, record the source URL, download date, and any subsetting parameters in the metadata file.

7.5 Spatial Data Processing in R

A standard workflow for processing spatial data:

Code 
library(sf)
library(terra)
library(tidyverse)

# --- 1. Load vector data ---
sites <- st_read("data/spatial/raw/sampling_sites.gpkg")
watershed <- st_read("data/spatial/raw/claremont_watershed.gpkg")

# --- 2. Verify and harmonize CRS ---
st_crs(sites) == st_crs(watershed)
sites <- st_transform(sites, crs = st_crs(watershed))

# --- 3. Spatial operations ---
# Clip sites to watershed boundary
sites_in_watershed <- st_intersection(sites, watershed)

# Buffer sites by 100 m
site_buffers <- st_buffer(sites_in_watershed, dist = 100)

# --- 4. Load and extract raster data ---
dem <- rast("data/spatial/raw/dem_10m.tif")
sites_in_watershed$elevation_m <- extract(dem, sites_in_watershed)$value

# --- 5. Save processed outputs ---
st_write(sites_in_watershed,
         "data/spatial/processed/sites_with_elevation.gpkg",
         delete_dsn = TRUE)

7.6 Map Production

All maps for reports and publications must include:

  1. Title and date
  2. Scale bar (use tmap::tm_scale_bar() or ggspatial::annotation_scale())
  3. North arrow
  4. Legend with clear labels and units
  5. Data source attribution
  6. CRS/projection noted in the caption or margin
Code 
library(tmap)

tm_shape(watershed) +
  tm_polygons(col = "lightblue", alpha = 0.3) +
  tm_shape(sites_in_watershed) +
  tm_dots(col = "elevation_m", size = 0.5,
          palette = "viridis",
          title = "Elevation (m)") +
  tm_scale_bar(position = c("left", "bottom")) +
  tm_compass(position = c("right", "top")) +
  tm_layout(
    title = "Sampling Sites: Claremont Watershed",
    title.size = 1.2,
    legend.outside = TRUE
  )

7.7 Metadata Documentation

Each spatial dataset must have a metadata record. At minimum, include:

Field Description
Title Descriptive name of the layer
Abstract Brief description of content and purpose
CRS EPSG code and full name
Extent Bounding box coordinates
Source Origin of data (URL, agency, field GPS)
Date created/acquired When the data were generated or downloaded
Processing steps Summary of transformations applied
Contact Analyst name and email

For formal publications, use FGDC or ISO 19115 metadata standards. For internal projects, the table above saved as a text file alongside the data is sufficient.

7.8 GPS Data Import

When importing GPS waypoints or tracks:

  1. Download data from the GPS unit to data/spatial/raw/ in GPX format
  2. Import into R using sf::st_read("file.gpx", layer = "waypoints")
  3. Verify coordinates visually by plotting over a basemap
  4. Assign the correct CRS (typically EPSG:4326 for consumer GPS units)
  5. Document the GPS model, estimated accuracy, and collection conditions

8 Quality Control and Acceptance Criteria

  • Verify CRS for every imported dataset before analysis
  • Check that point locations fall within expected geographic bounds (e.g., all sites should be within the study area polygon)
  • Validate topology for polygon layers (no gaps, overlaps, or self-intersections) using sf::st_is_valid()
  • Compare GPS coordinates with known reference points when available
  • Document the spatial accuracy and precision of each data source

9 Waste Disposal

Not applicable (computational procedure).

10 References

Lovelace, R., Nowosad, J., & Muenchow, J. (2019). Geocomputation with R. Chapman; Hall/CRC. https://doi.org/10.1201/9780203730058
Pebesma, E., & Bivand, R. (2023). Spatial data science: With applications in R. Chapman; Hall/CRC. https://doi.org/10.1201/9780429459016

11 Training Record

Training is documented in the EA Program training log. Before working independently with spatial data, personnel must:

  1. Read this SOP in full
  2. Complete the RStudio and GitHub orientation (SOP-L801)
  3. Demonstrate proficiency with sf and basic spatial operations in R under supervision
  4. Produce a correctly formatted map with all required elements
  5. Be signed off by the PI or GIS lead