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What are the most common GIS data cleaning techniques?

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If you work with geographic information systems (GIS), you know how important it is to ensure the quality and accuracy of your spatial data. Data cleaning is the process of identifying and correcting errors, inconsistencies, and anomalies in your data sets, such as missing values, duplicates, outliers, or invalid geometries. Data cleaning can improve the reliability and usability of your data for analysis and visualization purposes. In this article, you will learn about some of the most common GIS data cleaning techniques and how to apply them using various tools and methods.

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Milan Janosov

?? Founder @Geospatial Data Consulting | ??? Data Scientist | ?? #1 Best Seller Author on Amazon | ?? PhD in…

1 Identify data errors

Identifying potential errors and issues in data sets is the first step in data cleaning. Depending on the source and format of your data, you may encounter format errors related to the structure, syntax, or encoding of your data files; attribute errors related to the values or properties of your data attributes; or geometry errors related to the shape, location, or topology of your spatial features. To identify these errors, you can use various tools and methods such as data validation, exploration, and comparison. Data validation involves checking your data against predefined rules, standards, or specifications. Data exploration examines your data visually or statistically to detect any anomalies. Data comparison compares your data with other sources of information to verify accuracy, completeness, or consistency. Automated tools can be used for each process and generate error reports or logs.

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Milan Janosov

?? Founder @Geospatial Data Consulting | ??? Data Scientist | ?? #1 Best Seller Author on Amazon | ?? PhD in Network Science | ??? Forbes 30u30 | ?????? LinkedIn Learning instructor
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Assuming you managed to parse the data - for instance, as a table in Pandas or GeoPandas - I believe the geometry column will be the trickiest. This is kind of obvious since it's the new tech layer on top of a regular tabular data set. The usual first cleaning questions are like i) Does it have the right crs? ii) does it have the correct geometry types or a mix of points, polygons, multistrings, etc? iii) do the polygon boundaries overlap with what you were expecting? Of course, missing values can be a problem, and sometimes, tricky edge cases, like a LineString consisting of only one Point, can be a trouble on some platforms.

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Maria Ines Lopez R

Open to Innovation initiatives that couple AI and GIS solutions to the public and private industry
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In my experience performing an Exploratory Spatial Data Analysis (ESDA) it is a very useful way to get a first approach to the data. In this way you can visualize the data and identify out of place points, lines, polygons. It also, helps to understand the distribution of the population and catch errors (for examples variables that should add up to 100% goes way beyond, units conversion errors, lat/long and CSR problems etc). Common visualizations to check and understand the data include histograms, quantile maps, scatter plots, lines, and box plots.

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2 Correct data errors

The second step in data cleaning is to correct the errors and issues that you have identified in your data sets. Depending on the type and severity of the errors, you may choose different strategies to correct them. Data editing is the process of manually modifying data values or features to fix errors, which can be done with data editing tools or software. Data imputation is replacing missing or invalid values with plausible values, such as mean, median, mode, or nearest neighbor values, which can be done with data imputation tools or software. Data transformation is changing the format, structure, or representation of your data to make it more consistent, compatible, or standardized, and this can be done with data transformation tools or software.

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Milan Janosov

?? Founder @Geospatial Data Consulting | ??? Data Scientist | ?? #1 Best Seller Author on Amazon | ?? PhD in Network Science | ??? Forbes 30u30 | ?????? LinkedIn Learning instructor
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While I do 99.9% of my geospatial data work in Pyhton, sometimes it is easier to manually correct one-off errors or use a GIS software, like Google Eart Desktop or QGIS. However, if you are working on a product/pipeline, not a demo/one-off consulting project, then its still worth the effort to solve the issue programmatically.

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3 Optimize data quality

The third step in data cleaning is to optimize the quality and performance of your data sets. Depending on your data analysis and visualization goals, you may want to apply some additional techniques to enhance your data, such as data integration, data reduction, and data normalization. Data integration involves combining different sources, formats, or scales to create a more comprehensive, coherent, or harmonized data set. Data reduction involves removing or simplifying redundant, irrelevant, or noisy data to reduce the size, complexity, or variability of your data set. Data normalization involves adjusting or scaling data values to make them more comparable, proportional, or balanced. By applying these common GIS data cleaning techniques, you can ensure that your data is ready for analysis and visualization.

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Milan Janosov

?? Founder @Geospatial Data Consulting | ??? Data Scientist | ?? #1 Best Seller Author on Amazon | ?? PhD in Network Science | ??? Forbes 30u30 | ?????? LinkedIn Learning instructor
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Here, my all-time favourite move is the simplify function of GeoPandas, which can speed things up super quickly! Additionally, spatial indexing, like using Uber's H3, can also help with computational power.

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4 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?

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Maria Ines Lopez R

Open to Innovation initiatives that couple AI and GIS solutions to the public and private industry
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It is important to remember that the opportunities to bring data together from diverse sources also make it more relevant and, at the same time, challenging to maintain the quality of the data to very high standards.

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What are the most common GIS data cleaning techniques?

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1 Identify data errors

2 Correct data errors

3 Optimize data quality

4 Here’s what else to consider

Information Systems

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What are the most common GIS data cleaning techniques?

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1 Identify data errors

2 Correct data errors

3 Optimize data quality

4 Here’s what else to consider

Information Systems

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