Do you have a strategy for ensuring data accuracy in your GIS projects?

1 Define your data requirements

Before you start collecting or acquiring data for your GIS project, you need to define your data requirements clearly. This means identifying the purpose, scope, and objectives of your project, as well as the spatial and temporal extent, resolution, and accuracy of your data. You also need to consider the data sources, formats, and standards that you will use, and how they will affect your data quality and compatibility. By defining your data requirements, you can avoid unnecessary or redundant data, and ensure that your data meets your project needs.

2 Check your data sources

Not all data sources are created equal. Some may have more reliable, accurate, and up-to-date information than others. Some may have different metadata, projections, or attributes than others. Some may have errors, gaps, or inconsistencies that can affect your data quality. Therefore, you need to check your data sources carefully before you use them in your GIS project. You can do this by reviewing the metadata, documentation, and history of your data sources, and by comparing them with other sources or references. You can also contact the data providers or authors to verify or clarify any information that you are unsure about.

3 Perform data cleaning and editing

Even if you have checked your data sources, you may still need to perform some data cleaning and editing to improve your data accuracy. Data cleaning and editing involves identifying and correcting any errors, anomalies, or discrepancies in your data, such as missing values, duplicates, outliers, misalignments, or incorrect formats. You can use various tools and methods to perform data cleaning and editing, such as data validation rules, topology checks, attribute queries, spatial joins, and editing tools. You should also document any changes that you make to your data, and keep backups of your original data.

4 Apply data standards and conventions

One way to ensure data accuracy and consistency in your GIS project is to apply data standards and conventions. Data standards and conventions are sets of rules or guidelines that define how data should be collected, stored, processed, and shared in a GIS environment. They can cover aspects such as data formats, projections, coordinate systems, metadata, attributes, naming, symbols, and styles. By applying data standards and conventions, you can ensure that your data is compatible, interoperable, and understandable for yourself and others. You can use existing data standards and conventions from reputable sources, such as ISO, OGC, or FGDC, or you can create your own based on your project needs.

5 Test and verify your data accuracy

The final step to ensuring data accuracy in your GIS project is to test and verify your data accuracy. This means comparing your data with other sources of information, such as ground truth, reference data, or expert opinions, to assess how well your data matches reality. You can use various techniques and measures to test and verify your data accuracy, such as error matrices, accuracy assessments, root mean square errors, and kappa coefficients. You should also document and report your data accuracy results, and communicate any limitations or uncertainties that may affect your data quality or interpretation.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?