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The need for accurate data collection when mapping and reporting.

A recent study by Landscape Ecology titled ‘Overselling overall map accuracy misinforms about research reliability’ suggests that maps play a crucial role in deciding the end result of any project, be it scientific, land development, or landscaping. The report suggests that a map and collected data with sub-standard accuracy may lead to the derailing of an entire project, or result in potentially huge costs due to poorly informed decision making, often based on assumptions. For example, it is common practice for building and landscape architecture to sketch up initial concept drawings using pen and paper. These drawings can quickly convey ideas and concepts to a client, but using these for estimating material and construction costs would lead to inaccurate estimates.

GIS (Geographic Information System) is the software answer to this. Typically used in various geological and environmental fields to identify patterns, abnormalities and changes, leading to the identification of potential problems. Mapping and analysis of spatial, temporal and tabular data collected for an area requires an understanding of the data metrics being sought and precise software, and occasionally hardware, tools that reflect the level of detail required for the data to provide valuable inputs to a project.

The Landscape Ecology report on thematic map accuracy goes on to conclude that landscape ecologists can adopt three simple rules to improve their use and interpretation of map data. The first of these outlines the need for rigorous, well-established standards for sampling, responding and analysing data. The second focuses on class-specific accuracy, dependent on the complementary measures that are commissioned with the user’s institute. The third states that, in order to report a strong dataset, the report must be accompanied by standard error indicators which show the limits, and complexities, of the dataset gathered.

Creating maps for map specialists.

We’ve discussed this in the past, but to reiterate, GIS departments often collaborate with, and provide specialist technical expertise to other teams, sourcing data, performing analysis and preparing maps. For the uninitiated, GIS departments are comparable to the draughts person that draws up the design of your home provided by an architect, working alongside the builders that deliver the goods. Only these draughts people typically respond to their environment by mapping data collected and provided by a number of sources, and a design engineer would in the case be the architect.

This relationship then relies on accurate reporting between the ‘architect’, the ‘builder’ and the GIS department (or, often, GIS business, a separate entity entirely). Mistakes creep into the reporting metrics when either the technology or the observer, at any stage, isn’t up to the task of reporting on-site data to a high level of detail. Datanest puts the power in your hands by giving you common GIS tools and related data gathering methods.

The role Datanest was designed for.

To return to the report’s conclusions, an accurate mapping tool must have a standard for sampling, provide measurable accuracy metrics, and error indicators. Let’s explore all three.

Rule Number 1: Rigorous data entry and measurement.

Datanest is designed to mimic the journey from first sample testing to the final reporting phase, although the first three stages will prove our point here; Gather, Maps and Hub (with Evalu8, an additional step that can be added for lab sampling). 

Within Gather, you’ll notice that the module is split between two view options, one that looks like an app creator and the other a map. Separate from the Datanest mapping tool, the map in Gather allows the user to plot data points and create areas or lines for a sample or data entry item. The app editor decides which metrics are important to gather by creating a mini ‘app’ that someone who collects the data at a site can access and fill in. Data entry is consistent and with the use of predefined options and form logic, largely error-free from the start.

Using decades of experience in land contamination, ecology and geotechnical industries, the team at Datanest have refined collection apps to quickly and effectively gather data in the field. For example, our borehole collection app not only records a soil log, but it contains specific calculations that converts shear vane values, collates soil composites, moisture conditions, groundwater encountered, and Scala data. A further report on existing boreholes would include any existing tubing, water extraction, gasses present and other environmental factors — the metrics needed, for example, to determine the scale of the Christchurch water leak problem affecting the main drinking supply can be created in a formal and templated manner. The app itself is insistent in making sure the data collected is accurate and error-free. Data inputs can include expected parameters or allow a prescribed deviation from expected numbers, allowing a team to confidently collect recognisable and likely data metrics. Datanest Hub is where that data is analysed, and outliers considered before or during the mapping process can be identified.

Rule Number 2: Class-Specific Accuracy.

The example outlined above leads to the second rule, that of class-specific accuracy. Datanest comes preloaded with a number of reporting metrics specific to geotechnical, ecological and land contamination metrics. These include; gINT soil and bore logs, RSLog, Seequent, COreGS, SLAM, ground monitoring, initial geotechnical hazard inspection, well purging and health & safety forms, among others. For the purposes of map creation, Datanest Maps also includes many historic base maps and site maps that can be loaded, including any preexisting historical testing.

The purpose of these tools is to give the user as many accurate reporting metrics as possible in a very short time frame, allowing rapid deployment of field staff to respond to incidents, or simply to reduce time and cost on a project. Datanest pre-set apps allow users to use those which comply with their industry standard instead of going to their GIS department each time a new data collection form is needed. The additional app editor allows for any site testing outside the normal scope perimeters of the user’s field, as creating a field data collection app is a simple drag and drop process.

Rule Number 3: Error Reporter.

As we alluded to in rule number 1, the app editor comes with specific metrics that are calculated to look out for mistakes and inaccuracies, such as an inconsistent borehole depth, the presence of unlikely substances and duplicated results. The app will refuse to record the data, or the resulting duplicate will show up as such in Hub, where any other data ‘oddities’ can be filtered out. This process is acting as a real-time data verification tool, and again reduces the potential errors in reporting further down the line, or costly data cleansing.

A mapping tool for almost anything.

Datanest’s mapping ability mimics much of what a GIS department can do, up to the point where a map created on Datanest can be shared with clients to represent site findings accurately. Increasingly, we’re adding to both the do-it-yourself, analytical, and accurate reporting value of Datanest with new tools for drawing service layers and importing various data formats into Datanest. We’re also continuing to add more data analysis techniques with heatmaps, buffering tools and new regional base maps launched at the end of last year. 

Ultimately, accuracy is a matter of degrees. The closer to a true representation a GIS or sitemap can get, the more value it adds for its users and anyone invested in the project. And that’s something that we’re particularly interested in pursuing further.

Sites sourced:

  • Mas, Jean-François, Pérez-Vega, Azucena, Ghilardi, Adrián, Martínez, Silvia, Octavio Loya-Carrillo, Jaime, Vega, Ernesto. “A Suite of Tools for Assessing Thematic Map Accuracy” in Geography Journal. Date Published: 14th May, 2014. 
  • Stehman, S. V., Wickham, J. “A guide for evaluating and reporting map data quality: Affirming Shao et al. “Overselling overall map accuracy misinforms about research reliability” ” in Landscape Ecology, Iss. 35, pp. 1263-1267. Date Published: 28th May, 2020. 
  • “What is GIS?” on esri. Date Accessed: 10th March, 2023. Site Link:
  • “What is a Thematic Map? 6 Types of Thematic Maps” on maptive. Date Published: 19th October, 2020. Site Link: