You're facing conflicting spatial data in your GIS project. How will you navigate through the discrepancies?

Rigorous data assessment is crucial for navigating these discrepancies. Start by evaluating the metadata of each conflicting data source, including lineage, accuracy assessments, and collection dates. Identify potential reasons for the discrepancies, such as differences in methodology, projection systems, or data updates. Perform spatial comparisons to visualize the inconsistencies and assess their significance for your project. Through this comprehensive data assessment, you can determine the most reliable source or employ data integration techniques to reconcile the discrepancies and ensure the accuracy and robustness of your GIS analysis.

Assess the conflicting data by checking its source, accuracy, and date of collection. Reliable data sources are fundamental in GIS; they determine the quality of your analysis. Evaluate the metadata, which provides information about the data's origin, scale, and collection methodology. Sometimes discrepancies arise from outdated or low-resolution data. By identifying the strengths and weaknesses of your datasets, you can determine which data to trust and whether you need to acquire more up-to-date or precise information.

Metadata is key. As a public officer GIS database supervisor, when I received a new GIS data, my first assessment was checking the metadata. If they were not conform, especially to the INSPIRE model, I would consider the data as non-valid, and send them back to the producer, asking for rigorous metadata information.

In my experience, you assess metadata for: i) variations in map projections of the individual layers. ii. Variations in coordinate systems. iii. Variations in the scale at which the geographical objects are compiled. Also assess data for Internal errors within the data, including digitization errors, errors in field observations, errors resulting from sampling in satellite images, and aggregation errors Then reconcile data while ignoring inaccurate data.

Reliable data sources are essential and metadata plays a vital role in understanding their origin, scale, and collection methodology. To ensure accuracy, consider steps such as evaluating data currency, assessing data resolution, analyzing data consistency, investigating data lineage, consulting data documentation, verifying data quality metrics, exploring alternative data sources, and documenting your evaluation. By following these steps, you can make informed decisions about which data to trust and make informed decisions about whether to acquire more up-to-date or precise information. By following this rigorous evaluation process, you can confidently determine which data to trust and make informed decisions.

Comparing contradictory layers in GIS software is an effective approach to detecting discrepancies and inaccuracies. Overlaying the layers allows you to visualize geographical relationships, identify anomalies, detect overlaps and gaps, analyze spatial patterns, and investigate data discrepancies. Some common GIS tools for layer comparison include ArcGIS, QGIS, etc. When comparing layers, consider the following tips: Use suitable symbolism. Adjust the transparency and opacity. Investigate alternative visualization possibilities and make use of analytical tools.

It's quite an unpleasant moment, when you are facing conflicting spatial data in your GIS project. I think the first step is to verify the accuracy of the data. Begin by cross-referencing the updated dataset with authoritative sources, such as government records, official maps, or recent satellite imagery. Then I also suggest conducting field surveys (if possible). Additionally, I can recommend to use local knowledge and consult with local experts who have detailed knowledge of the area. Their insights can be invaluable in identifying discrepancies and ensuring data accuracy.