Collecting Data in the Field

Overview

Questions

• When should I collect my own geospatial data?
• How do I design a form so that field data is consistent and easy to analyze?
• How accurate is GPS on a phone, and why does it matter?
• What ethical questions come with collecting data about people and places?

Objectives

• Name common methods and apps for field data collection
• Design a data schema with clear fields, data types and dropdown choices
• Explain the main sources of GPS error and how to record accuracy
• Plan for consent, privacy and safety before going into the field

Why collect your own data?

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Existing datasets rarely describe exactly what a social scientist needs. A city may publish where bus stops are, but not whether they have shelters. A business list may include grocery stores, but not whether they sell fresh produce or accept SNAP benefits. When the data you need does not exist, is out of date, or cannot be trusted, you collect it yourself.

Common methods include:

Method	What you record	Example
Observation	Features and their condition, at a location	Sidewalk condition, store amenities, vacant lots
Surveys and interviews	People’s answers, often linked to a place	Where residents shop, and how they get there
Participatory mapping	Places people draw or point to	Areas residents consider unsafe, or important
Digitizing	Features traced from maps or imagery	Buildings visible on a CORONA image
Sensors	Measurements at locations	Air quality, noise, temperature

Apps for field data collection

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Most field data is now collected on a phone or tablet, with forms that record a location automatically.

App	Notes
QField	Free and open source; uses projects prepared in QGIS; works offline; Android, iOS, Windows, macOS and Linux
Mergin Maps	Also based on QGIS projects, with built-in syncing
KoboToolbox and ODK	Free survey tools widely used in research and humanitarian work; strong for questionnaires, with GPS points
ArcGIS Field Maps and Survey123	Esri’s apps; require an ArcGIS account, which many universities provide

This module uses QField, because it is free and works directly with QGIS. The grocery access case study in the Case Studies module walks through a complete QField project.

Design the data before you collect it

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Field data collection fails most often not in the field, but at the desk beforehand. If every person invents their own categories, the result cannot be analyzed. Before anyone goes out, decide on a schema: the list of fields, their types and their allowed values. The Data Management module’s Designing Your Data episode shows how to write one as a data dictionary, with field types, domains and unique IDs. For field work, a few rules matter most:

• Use dropdowns (also called domains or value maps) wherever possible. Free text produces “SNAP”, “snap”, “EBT” and “yes” for the same thing.
• Always include Unknown. Guessing introduces bias; “Unknown” is honest.
• Make essential fields required, so a record cannot be saved without them.
• Write a placement rule for points, for example “stand at the main public entrance”. Without one, some people will mark the middle of the building and others the parking lot.
• Test the form on a few real sites before the full collection.

The QField demonstration in the Case Studies module shows a complete schema for mapping grocery stores.

How accurate is GPS?

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Phones use GNSS (Global Navigation Satellite Systems: GPS, Galileo, GLONASS and BeiDou) to find their position. Under open sky a modern phone is usually accurate to about 3 to 5 meters, but accuracy gets much worse:

• next to tall buildings, which block satellites and bounce signals (called multipath)
• under dense trees
• indoors
• in the first moments after the app starts, before enough satellites are found

Field apps such as QField show the current accuracy as a circle around your position and a number in meters. Wait until it settles, set a target (for example, under 10 m), and record the accuracy with every point. External GNSS receivers can reach sub-meter or even centimeter accuracy, at extra cost.

How far off could a recorded point be?

Does a few meters matter? For counting stores in a county, rarely. For measuring walking distance to a store entrance, or deciding which side of a boundary a store is on, it can.

Ethics, privacy and safety

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Collecting data about places often means collecting data about people. Before you start:

• Get approval. Research with human participants (surveys, interviews, photos of people) usually needs review by your institution’s Institutional Review Board (IRB).
• Ask for consent, and explain how the data will be used and shared.
• Protect privacy. A precise location can identify a person or a household, so avoid recording home locations unless you must. The Data Curation module’s Ethics and Sensitive Location Data episode covers how to store, mask and share sensitive locations.
• Respect property and people. Stay on public land unless you have permission. Do not photograph people without consent.
• Stay safe. Work in pairs, tell someone your route, and carry a charged phone.
• Keep field notes. Record the date, the team, the weather, problems and decisions. They become part of your metadata.

Check the data quickly (quality assurance)

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Review the data the same day while you still remember what you saw: are required fields filled in, do points fall inside the study area, is any place recorded twice, are categories used the same way by everyone, and do photos match their records? The Data Management module’s Data Quality episode explains quality checks (and the difference between quality assurance and quality control) in more depth.

Challenge

Design a schema

You are mapping bus stops in your city to study accessibility for wheelchair users. List five fields you would collect, with their type and allowed values.

Show me the solution

One possible schema:

Field	Type	Allowed values
stop_id	Text	Code shown on the stop sign, to match the transit agency’s data
shelter	Dropdown	Yes, No, Unknown
bench	Dropdown	Yes, No, Unknown
curb_ramp	Dropdown	Yes, No, Unknown
surface	Dropdown	Paved pad, Grass or dirt, Other
photo	Attachment
notes	Text

A placement rule, such as “stand at the stop sign”, keeps the points consistent.

Challenge

Spot the problems

Three volunteers mapped the same neighborhood. Here are some of their records:

store_name	store_type	snap_accepted	gps_accuracy_m
Fresh Foods	supermarket	yes	4
Fresh Foods Market	Supermarket	EBT	6
Corner Stop	gas station		38

What problems can you find, and how would a better form have prevented them?

Show me the solution

• The first two rows are probably the same store entered twice with different names. A QA check for nearby points with similar names would find it.
• store_type uses inconsistent capitalization and a category (“gas station”) that is not on the list. A dropdown prevents both.
• snap_accepted has “yes” and “EBT”, and one blank. A Yes, No, Unknown dropdown, made required, fixes this.
• An accuracy of 38 m is poor. The volunteer should have waited for a better fix, or written a note.

Key Points

• Collect your own data when what you need does not exist or cannot be trusted
• Design the schema first: clear fields, dropdowns, an “Unknown” option and a placement rule
• Phone GPS is typically accurate to a few meters under open sky, and much worse near buildings and trees; record accuracy
• Plan for consent, privacy and safety before collecting data about people and places
• Check data the same day it is collected