Introduction

If you've never conducted a field survey or collected data before, you'll find the tips you need in this guide to confidently conduct your first field data collection and mapping project, including:

  • A guide to learning the ins and outs of field data collection
  • Best practices for designing your survey
  • Tips for field data collection
  • A glossary of common data collection, mapping, and analysis terms

It's everything you'll need to get started collecting better data with forms and surveys.

If you have any questions, concerns, or comments, feel free to reach out to us.


The value of field data

While most organizations collect some form of data in the field — whether it’s asset inventory, sales meetings, or facility inspections — many don’t make effective use of that data throughout their organizations. Data captured in the field is some of the most valuable business information at a company’s disposal. It reflects what’s happening at the point of transaction or the real picture of what’s going on at the job site.

Field data collection is one of the best ways to gain insights into the facts of an operation, such as:

  • What condition are our assets in?
  • Have we performed safety audits on all our job sites?
  • Are we in compliance at all our facilities?
  • Which work orders are completed and which aren’t?

Whether they’re inspectors, auditors, technicians, or salespeople, empowering your field staff with tools to contribute information they collect into an institutional knowledge base can benefit everyone.

With a well-planned and -designed data collection process, you’ll be able to get rapid feedback from the field to save money, stay in compliance, and create more value from field operations and investments throughout your organization.


The importance of spatial dimension in field data

Geographers use a variety of tools to collect, process, and analyze their data to create maps, charts, and graphs that empower decision makers to make more informed choices. Collectively known as GIS (geographic information systems), these tools make use of spatial data, or data collected with a spatial component, in order to plot information on maps. Collecting this data can be time-consuming, but it has massive value for an organization.

The boom in location data available today has expanded the market for geographic data and tools. GIS software gives analysts the ability to use location data and spatial relationships to derive novel information. For many businesses, maintaining an accurate inventory is crucial for knowing what items are in stock, but that same data can be used in ways that make the business even more efficient.

For example, if one product sells more frequently in some locations but not in others, spatially tagged sales data can inform management to stock more in those locations, in order to maximize the number of products sold and reduce wasted shelf space. For decades, data analysis has focused on the “what” and “why” of data, by including “where.”


Types of field data collection

The phrase “field data collection” may sound esoteric, but almost every business in the world does it in one form or another, whether it’s by taking inventory, running through a checklist, or inspecting physical assets.

Here’s an example:

When UPS collects a signature to acknowledge receipt of a package, they’re collecting field data — specifically: where the parcel was delivered, at what time, and to whom.

Some more examples of different types of field data collection:

  • A census worker collects information about how many people live in an area, their age, gender, and other demographics.
  • A honey farmer collects information relevant to the health of their hives, such as whether or not a hive has been or needs to be treated for mites.
  • A civil engineer might collect information about the condition of a road or bridge.

Visit our customer stories to see how companies across dozens of industries collect field data with Fulcrum.

And in most cases, the quicker the field data can get back to the office and be analyzed, the better.


Different methods of data collection

Historically, it’s been a challenge to maintain close ties between a field service operation’s ground-level personnel and upper-level management. Pen and paper data-capture processes using triplicate forms meant that results couldn’t be delivered to decision makers without first going through a time-consuming process of transcription, cleanup, aggregation, and report-building. Often, by the time the results reach into the higher echelons, it’s been months since the information was collected.

Today, mobile technology allows more timely flow of intelligence from the field, which allows managers to make better decisions with up-to-the-minute answers to their questions.

In addition to the problem of communication lag, collecting field data with pen and paper can be a burdensome process. Depending on your industry, your field technicians might be carrying a clipboard with pen and paper, a camera, a GPS unit, a laptop, and more. Even across the different methods of data collection, using Fulcrum, all a surveyor needs is their mobile device — which most people are already carrying.

Here’s another benefit to mobile data collection:

After you initially deploy a survey to a collection team, there will usually be modifications to make after it’s in the field. In the days of pen and paper surveys, this was naturally a hassle to deal with, requiring editing PDF templates and reprinting new batches of forms to distribute. As a result, organizations using a method like this are tempted to overload their survey with every possible thing. Because of the inefficiency of making changes to paper surveys, many positive survey structure changes would never be made.

With digital tools like Fulcrum, updating survey template structures is more realistic. Given that the survey on a collector’s device is digital, syncing with the server allows the user to receive the latest version automatically, without any need for memos or meetings to bring a team up to speed.


How to build surveys for field data collection

How you set up your data-collection survey will be critical to its success. The way you design how observations are recorded will have a tremendous impact on the way the data is collected and how it is later processed and analyzed. An effective survey is one that is specific and focused. You don’t want to go on a tangent away from your original purpose. “Nice to have” information can quickly take away from the precious time it takes for your team to conduct the survey; it should be scrutinized as to whether it really needs to be captured.

For your questions, provide appropriate answer choices to pick from. This includes framing your questions fairly and being inclusive while also keeping them brief and direct. Consider how the data will be processed and analyzed as you’re designing your survey. For example: do you want to allow a question to be answered with an open-ended response, or provide a range of structured answers to choose from instead?

Other things to consider while designing your survey include whether you will need supporting reference information while collecting the data. If a field user needs to verify that they’re within a certain area while collecting the observation, how will they be able to confirm that? Design and logistics go hand-in-hand. For example, will you be working in an area requiring access to cellular connectivity? (Detailed tips and suggestions can be found in a later section on Survey Best Practices.)

The best approach to building a survey is to work backward. Identify what you want to learn and how you want to measure the results before you begin designing. Much of effective survey design is about not just what to collect, but also how it’s collected. Thinking about the end goal keeps you focused on results first, and process second. Starting at the end lets you create a process that matches your desired end state. Once you’ve identified what “success” looks like, you can analyze the details of what observations you need to collect and in what order.

When designing your survey, consider the following questions:

1. What do I want to know?
The first step is to identify the information you need to reach a successful result — does your data answer all the right questions? By starting at the end, you can visualize how you want to present your information. Create a hypothesis that is testable and measurable, and from that determine what data you need to collect.

2. What metrics should I use?
The next step is to identify metrics that will guide your decision making. Identifying these before conducting your survey will ensure objectivity in your survey by avoiding bias through after-the-fact analysis. The point in conducting a survey is to gain real-world knowledge, not to prove a specific point. Your data should be factual information that you can then use to derive answers. Identifying these key indicators will feed into the specific data to capture in your survey. If your data supports your hypothesis, great! If it doesn’t, it’s never advisable or ethical to bend the data in a way to support your position. Also, avoid framing questions in a way that leads people to a desired conclusion, in order to avoid bias in your data.

3. What data should I collect?
Next, you should break down your desired result into its individual components. Perhaps to answer your questions, your survey needs 25 different pieces of data. In that case, organize them in a fashion that makes it efficient for a field collector to fill out the survey. People often make two major mistakes in this process: They either try to collect everything they can, or as little as they can, and both of these approaches are potentially problematic. Collecting too little data can make your survey lack enough information for meaningful answers. Collecting too much data can lead to people terminating a survey midway through. Both have their advantages, of course. A short and sweet survey enables rapid data capture, but will lack depth. An incredibly detailed survey delivers a lot of information, but takes longer to perform. Get a feel for the time it takes to complete the survey by conducting some mock collection. The goal should be to strike the right balance of data depth and efficiency. Try to isolate the fewest number of key data needed for your project, and add additional fields or a comment section as a catchall for general use. It’s a good idea to have general “comments” fields for collectors to leave notes or other insightful information from the field.

4. How should I lay out my survey?
The way you format your survey can help save time and reduce the work needed to collect data in the field. We’ll focus on four major ways to improve the collection speed of your survey: Chunking, Labeling, Skip Logic, and Calculation Fields.

Chunking is the grouping of similar questions to help organize your survey and allow it to flow easier. Grouping related questions into sections helps collectors follow the process. If you have five questions on history and five questions on politics in your survey, separating them into two groups makes logical sense. It simplifies the flow for your field collection teams.

Labeling is the method of adding a label to chunks of questions you’ve already grouped together. This helps the surveyor understand the purpose behind the various of blocks of questions they are collecting, and gives them an idea as to how far along they are in the survey. When a survey is well labeled, a surveyor can parse a long question set easily, and find a specific question without having to read each question and entry to find a piece of information later on.

Skip logic refers to the use of logical conditions to determine how the survey flows. Skip logic is key for keeping only the relevant questions in front of a collector. If a user answers a question that makes later questions irrelevant, skip logic allows you to bypass those questions entirely rather than wasting time having to scroll past them. In an ideal setup, a survey should only ever present relevant questions to the data collector.

Calculation fields allow you to conduct simple or complex calculations instantly by using other questions as inputs. For example, rather than having to tally up responses by hand, or use a calculator to determine the volume or area of a space, a calculation field can be used to take your initial measurements and generate an answer instantly. This is useful for individuals in engineering and construction who have to take specific measurements to create an estimate, right on site.

After defining the original purpose for collecting, gathering, and processing your data, you will need to analyze your results. Diving deep into the information collected from your survey provides the answer to what you were originally motivated to find out — and sometimes more! A thorough evaluation of the findings from your data may suggest conclusions which were only hypothesized before. If your analysis can replace subjective decision support with measurable facts, you will have more confidence in your conclusion.


Logistics of collecting field data

Having a clear and defined purpose for your field data collection project is imperative. On-the-ground surveys can be expensive, both in terms of time and money. You should clearly define your goals and objectives before conducting your data collection activities.

Define your purpose by analyzing precisely what data you need to collect to answer your question. There will likely be additional data you could collect while in the field but take caution against scope creep, or adding new work not contained in the original project. Any additional data collected can quickly increase costs and time spent conducting and completing your survey. If what you’re looking to learn can be determined back in the office, don’t include it in your field data collection process. For example, why collect information that can be pre-loaded into your survey for collectors to validate, rather than collecting it from scratch?

Your protocol should include training the personnel who will be collecting your data. The level of understanding and competency of your field staff will be reflected in the quality of your data. It will also be beneficial to have a long-term project timeline and schedule of daily activities. Identify what personnel will be on your data collection team and determine if they will need to work together.

Consider the logistics to safely and efficiently perform your survey. For example, your project may warrant a team of two — one driver and one person collecting data. You may require certain conditions regarding weather and seasonality to be met before you can or when you cannot conduct your survey. As you define your procedure you will need to determine if you’ll be collecting the data while driving in vehicles, on foot, or even remotely. Establish a schedule if there will be any revisit frequency to your data collection.

Identify the parameters and range of your survey and ensure your team conforms to the standards set. Going outside the identified parameters and range can skew your data and potentially compromise the results of your final analysis.

Before you depart for your data collection mission, plot your course. It may seem obvious that you know “where” you’re going to do your work, but there is more to it than that. Having a plan before you start gathering data in the field is an essential first step. A few things you’ll want to take into consideration are: how many hours in the day it will be possible to collect data, the spatial relationship between collection sites, and how long it takes to complete your survey at each location. Does it makes more sense to travel to the furthest location first and work backward? If you have multiple sites to visit, there will be an optimal route you can take rather than doing so in a random order.

Other logistics to include in your planning:

  • Do you have any needed permission, codes, or keys for access to sites on private property?
  • Will you need personal protection from the elements, pests, animals, etc?
  • What is your plan in the case of an emergency or injury?
  • (If traveling by vehicle) Is your vehicle properly maintained, capable, and with adequate fuel?

After you collect a batch of data, it may require processing for your eventual analysis or visualization. This step in the process can vary greatly depending on how the data is being collected and what’s required to make the raw data usable. Placing emphasis upon optimal design and an effective protocol step will benefit the processing piece of your workflow. If your protocol dictates that data will be collected with paper and pencil, you will require a post-collection processing task where results are transcribed into a computer spreadsheet or database. Each additional step in the processing portion of data collection can introduce opportunities for error. Whether it’s human error while transferring data from paper to electronic form, or using incorrect parameters in a programmatic conversion, each additional step added introduces an opportunity for error.


How to use basemaps

A core component of Fulcrum as a field data collection tool is the included set of basemaps. Fulcrum mobile users are provided map reference information from Google when they are within an Internet-connected environment. The Streets, Aerial/Satellite, Hybrid, and Terrain basemaps are available to choose from, as well as the ability to choose “None.”

There are situations where you will want to collect data in a disconnected environment and you require map reference information to complete your task. Not only does Fulcrum have the functionality to collect data offline but it also allows you to use interactive offline maps. There are examples on the website for using both the vector and raster GIS data types in your offline map creation.

Another basemap layer type which is available to the mobile user is Tile XYZ. If you seek access to layers hosted in a Tile XYZ tiling scheme, they can be added as a reference layer within Fulcrum.

Learn more about how choosing the right basemap can improve the quality of your data.


Exporting and integrating data

After your data has been processed and analyzed, you will likely want to distribute or publish the findings. There are various methods to convey your results: tabular, map, charts, graphics, etc. Often, a combination of methods will be helpful to tell your story but it is important to use the right approach in order to achieve maximum effectiveness.

Not all data collection workflows are created equally! Some tools are built upon platforms which allow your data to be instantly distributed in a variety of formats once it’s collected. There are also other integrations to consider, such as the ability to publish your data once and then connect it to other third-party services.

Exporting the data:
Not only does Fulcrum make it easy to import and collect data but it also has robust options for exporting your data. All Fulcrum plans support exporting data in these nine different formats: CSV, Microsoft Excel, KML (Google Earth), Esri Shapefile, GeoJSON, Esri File Geodatabase, SQLite, SpatiaLite, PostGIS. Along with these formats are various export options to control exactly what you need to export, including: filtering by geographic area or the date the record was created or updated and whether to export multiple apps and/or projects’ data at once.

Integrations with other platforms:
After you’ve built your survey, deployed it, and collected some data in the field, you will likely be ready to visualize, analyze, or process that data. One of the benefits of conducting field data collection with the Fulcrum platform is that your data is entered into a system that’s capable of powerful integrations with other services and platforms.

Here are a few examples of how you can leverage Fulcrum’s extensibility:

  • Data Shares
  • Fulcrum API
  • Data Events
  • Zapier

Survey best practices

Conducting surveys can provide useful answers to specific questions. Surveys don’t need to be complex or exhausting to provide valuable information and insights. If you’ve ever wondered how to conduct a good survey, these tips will help.

Keep questions relevant and specific
Ensure you’re asking for pertinent information only. Avoid unnecessary details, feelings, and opinions unless they are important to your investigation. Staying focused on data critical to your objectives also keeps the process working smoothly without unnecessary delay. The “nice to have” information is great to collect if you can, but you don’t want the collection of optional data to detract from your primary data points.

Make questions short
Keep your questions as direct and to-the-point as possible. The more specific the question, the less risk there is of a collector misreading the question and answering incorrectly. Breaking up a survey into many smaller questions versus a few long ones results in richer, more useful data in the end. An added benefit of short, direct questions is that the data capture process can flow with fewer delays and require less thinking from element to element.

Use simple language
Be clear and obvious about what you’re asking. Avoid flowery language, terminology, and euphemisms, and ask questions in an unbiased, objective fashion. Sometimes, technical jargon may be required, but keep in mind the audience that’s being asked to complete the survey. Ask yourself: Is this term something my target audience will understand? The best data collection surveys can often be deployed and used with minimal training of collectors in the specifics.

Ask about one thing at a time
One question, one answer. Ask for one thing at a time, and avoid open-ended questions without a clear answer. Breaking up a survey into more small questions rather than few large ones provides the added benefit of being able to slice up the data in myriad different ways once you’ve finished your project. Analysts can get more granular in querying the data for answers.

Avoid biased language and leading questions
Phrase your questions to be considerate, inclusive, and respectful. Don’t frame up or lead the question with unnecessary adjectives and adverbs that may bias an answer. Remain objective and avoid asking questions that may direct or lead the subject to answer in a particular way.

Put questions in a positive form
Phrasing questions negatively can be confusing and may bias your responses. It can be confusing to ask about what is not, so instead simply ask about what is. If you can reduce questions down to a binary “yes or no” question, that makes things even simpler for the collector to understand.

Consider the ordering of questions
The order in which you display the questions on your survey form can make the difference between correct and inaccurate data. This is where field testing helps you iterate your design process to get the structure just right. For ease of use, most of the time your questions should flow in the order the collector typically conducts the survey. If they’re doing a construction site walk-through inspection, structure the survey in the order the inspector moves through the site.

Offer a balanced set of responses
Give a range of answers that are inclusive and thorough. Avoid errors of omission by including multiple options that cover all possible responses to the question. Using an “Other” option within choice lists helps avoid getting overly specific with options that are predetermined.

Add a comments section for field notes
Sometimes in the field, you might find important notes, data, or additional information you may want to relay to the analysts. An optional comments section at the bottom of your survey allows the addition of important information that otherwise might have been overlooked. It functions as a good catch-all for anything your survey model might not have accommodated for.


Glossary

Address
A location of a residence or workplace, designated with numbers and/or letters, and arranged in a format for easy reference on a map.

Alphanumeric Grid
A grid with letter and number ordination used to easy locate places and features.

Annotation
Text, images, or notes on a map used to provide information to the user.

API
A set of commands, tools, and protocols used to interact directly with information in a program.

Assessment
An evaluation of something to determine its quality, quantity, or state.

Attribute
A piece of information which describes an object or feature.

Basemap
A foundational layer for a map that provides context and reference information for other data layers to be overlaid.

Biogeography
The study of how species are distributed across the planet and their evolution.

Boundary
A line which delineates an area.

Boolean Value
A data type or variable with two possible values: true or false.

Boundary Survey
An evaluation by measurements, conducted by a professionally licensed surveyor which establishes the corners of a legal parcel of land.

CAD
Computer-aided design - CAD, utilizes computer software to facilitate drawing, analyzing, and disseminating technical designs. It is typically used in architectural, engineering, and machining workflows.

Calculation Field
Calculation fields can be used to write simple expressions to calculate values dynamically based on inputs given to other fields in your forms. This can be simple ‘total’ calculations or complex equations referencing other calculation fields and even data contained in repeatable sections. Calculated field expressions can be written in JavaScript, even as complex functions (and entire programs).

Calibration
Readings and adjustments compared against a standard to ensure accuracy, typically with instruments or equipment.

Cartography
The science of map creation.

Cell
With spreadsheets or raster data, cells are the intersections of rows and columns.

Cartographic Generalization
The process of reducing the detail of the features being represented within a visualization.

Centroid
The center point of a geometric object such as a line or polygon.

Character
A letter or symbol.

Compass
A device which indicates the direction of magnetic north and references the bearing to the other cardinal directions.

Compression
A process run upon some data whereby it is transported or stored in a smaller size than its original composition.

Concatenate
To link or chain things together, in series.

Conditional
A concept in programming where different actions are executed depending upon whether a boolean value is met.

Contour
A line joining points of equal elevation, typically represented on a map or as a dataset.

Coordinate
A set of numbers representing a position.

Cultural Geography
The study of various aspects of human culture across the globe and throughout time.

Database
A collection of data that is stored in a manner which enables efficient retrieval.

Data Collector
A data collector can be a person and/or a device. The data collector records observations, states, or quantities of a particular subject.

Data Collection/Data Capture
A process whereby observations are documented according to a predetermined set of criteria.

Dataset
A collection of related data.

Data Type
The classification of data which determines what sort of values can be stored and how it can be used.

Datum
In Geodesy, a datum is a mathematical equation which defines a reference from which measurements can be taken.

Degree
A unit of measurement of angles.

Density
The distribution or quantity of something per unit of area.

Dimension
A physical measurement of a property of an object.

Distribution
The spatial arrangement of an object or species.

Edge
The outside limit of an object, area, or surface.

Elevation
The height above a given point.

Envelope
The box drawn around one or many features, also known as a Bounding Box.

Equator
The line of latitude that falls equidistant between the north and south poles, with the measurement of 0°.

Error
A mistake, inaccuracy, or miscalculation.

Extrapolation
The act of projecting a conclusion based upon existing data and current trends.

Export
In computing, an output of data in a particular format type.

Feature
A distinct attribute or property.

Form Field
An element of a record, representing a single piece of data. Form fields have different available types, including:

  • Text
  • Numeric
  • Yes / No
  • Label
  • Single Choice
  • Multiple Choice
  • Classification Set
  • Photo Fields
  • Video
  • Audio
  • Barcode
  • Date & Time
  • Sections

Filter
The process of including only a desired subset from the original dataset.

Geocoding
The process of deriving coordinates from a physical address.

Geodatabase
A database with the specialized ability to create, handle, and store geographic data.

Geodesy
The science and mathematics related to the shape of the earth, its position in space, and gravity.

Geography
The science and study of the earth, its inhabitants, physical features, and atmosphere.

Geometry
The branch of mathematics concerned with the properties and relations of shapes.

GIS
Geographic Information System — a collection of hardware, software, data, and personnel which together form a system for creating, updating, and disseminating geographic information.

GMT
Greenwich Mean Time. GMT is the former world time standard but has been since replaced by Coordinated Universal Time (UTC). They are generally considered equivalent.

GPS
A navigation system which consists of a network of orbiting satellites, ground control stations, and user receivers. One way signals are transmitted to users with positioning, navigation, and timing data as long as devices have a clear line-of-sight with four or more satellites.

Ground Truthing
Verifying a record of observation directly at the subject’s site.

International Date Line
The imaginary line running approximately along 180° longitude which demarcates one day from another.

Interpolation
The act of estimating a value within two known values.

Joining
When two or more columns from tables in a relational database are combined using values common to each.

Latitude
The angular distance of a point, north or south of the equator.

Layer
In GIS, a layer is a graphical representation of geographic data.

Legend
A map element which helps the user understand the symbology.

Longitude
The angular distance of a point, east or west of the Prime Meridian.

Mean
The average in a set of numbers; add together the numbers in the set and divide by how many numbers there are.

Median
The middle number in a set of numbers; if there are two numbers in the middle position, take the average of those.

Metes and Bounds
A method of documenting land (as property), in which “metes” describe the distances between points and “bounds” refer to the prominent features and general layout of the area.

Monitoring
A repeated process or workflow which observes or records something.

Nadir
The direction below a position — opposite of zenith.

Navigation
The practice of determining ones location and accurately orienting your course to an intended destination.

Noise
Distortions in a signal which interfere with the intended message.

Null Island
An invented geography with the coordinates 0°, 0°. Null Island is a concept derived from the results of geocoding data where the intended coordinates could not be matched for the entered address - resulting in a “0,0” being returned.

Parcel
A legal boundary of land ownership.

Plat
A map document which primarily details land ownership parcels, but also describes boundaries and other relevant reference information. Also known as a Cadastral Map.

Precision
The closeness of measured values.

Piloting
Testing or trial of a process before fully conducting it.

Qualitative
Of or pertaining to analysis of the quality of something.

Quantitative
Of or pertaining to analysis of the quantity of something.

Query
A detailed request for information made against some data.

Raster
A type of GIS data which consists of a series of gridded cells that contain values representing various information such as elevation.

Record
An individual collection of values in a larger dataset.

Sample / Sampling
An observation or collected record / the process of collecting data.

Scale
The conversion factor between the distance on a map and that actual distance on the ground.

Shapefile
Developed by the company Esri, a Shapefile is a combination of files which together represent vector geometry and attributes of GIS data.

SQL
Structured Query Language. SQL is the primary language for manipulating databases.

Surveying
The science of assessing and documenting lands for precise measurement and evaluation.

Symbology
In cartography, the use of characters, symbols, or styles for representation of meaning.

Spatial Data
A piece of information with a spatial or geographic component.

Table
Numbers or values arranged in rows and columns.

Theodolite
A surveying instrument for measuring horizontal and vertical angles.

Tile
Small and numerous images of map data that together combine to form a larger map — typically viewed on the web.

Tile Server
A collection of databases, libraries, and software which work together to return the correct map tiles to the requesting client.

URL
Uniform Resource Locator. URL is a reference to a resource on the Internet.

USGS
The United States Geological Survey — a scientific agency of the U.S. Government focusing on geography, natural sciences, earth science and biology.

UTM
The Universal Transverse Mercator is a coordinate system for the earth. UTM is not one single coordinate system but instead divides the earth into 60 different zones — each with their own projection.

Variable
A name which is assigned a value.

Vector
In GIS, features composed of point, line, or polygon geometry.

Waypoint
A point of reference generally associated with GPS units as stored coordinates with a few basic attributes.

WGS84
World Geodetic Survey 1984 — WGS is the standard coordinate system for the earth. It was established in 1984 and is used by GPS.

Zenith
The direction above a position — opposite of nadir.

Zoom
The action of increasing or decreasing a viewshed.