Wie würden Sie Konflikte lösen, die sich aus Diskrepanzen in den Daten ergeben, die von Außendienstteams in GIS-Projekten erfasst werden?

Resolving data discrepancies in GIS projects is like navigating different maps on a road trip—each might show different routes, and it’s up to you to find the right one. Start by ensuring everyone in the field uses consistent data collection methods. If discrepancies arise, overlay the conflicting data in your GIS software, much like comparing two maps side by side to spot differences. Engage with field teams to understand their process, similar to asking for directions when lost. Cross-reference the data with trusted sources to validate accuracy, just like checking an online map. Finally, learn from the experience—update protocols to prevent future issues, ensuring your GIS projects stay on track and reliable.

There are times (especially in mining) where the original observation point in the field, from blastholes etc are no longer available. Although we do not doubt the quality of that original data point we have no ability to validate. As a result for any interpretation you should create a data structure (folder set up of hierarchy in a software) that separates collection methods accordingly. When discrepancies then vary between methods use those which can be field verified as true and remove historical data points of less confidence when needed. This approach allows for simulations of models with data based on level of confidence of the input.

Data Validation is an important first step for any workflow with data. In my experience, I always begin by validating the collected data against predefined criteria or standards. For instance, in a recent project involving land-use mapping, I encountered conflicting data from different field teams. I used GIS software tools to flag inconsistencies and cross-referenced the data with reliable secondary sources like satellite imagery and historical records. Next time - just establish clear validation protocols before the data collection began. You'll save time and efforts.

To resolve conflicts from discrepancies in data collected by field teams in GIS projects, start by reviewing the conflicting data against verified sources or previous datasets. Communicate with the field teams to understand their data collection methods and conduct spot checks in areas with discrepancies. Reinforce standardized data collection protocols to prevent future issues, and document the resolutions for reference.

To resolve conflicts from discrepancies in GIS data, start by validating the data against predefined criteria and reliable sources. Use GIS software tools to flag inconsistencies and ensure accuracy. Establish clear validation protocols before data collection to streamline this process and minimize errors.

Field communication is not always obvious but this is the key to resolving discrepancies in GIS data. In my experience, when discrepancies are identified, I immediately reach out to the field team members involved to gain insights into the context and conditions under which the data was collected. For example, during a recent environmental monitoring project, we noticed inconsistencies in the vegetation data. By directly communicating with the field teams, we discovered that varying weather conditions had affected the data collection process. Regular check-ins and maintaining clear communication channels with the field teams was so helpful in this situation.

Find out the easiest way to communicate with field staff. Usually this can involve them having a device that has easy access to a quickly fillable digital form. You will need to make an effort to make it easier for field staff because they are often navigating difficult conditions outside. Things like a weekly team phone call, meeting, or email are good as long as it is quick and easy for field staff.

The most important thing is to present the same protocols for all teams, ensuring the information is clear and objective. Also, above all, all teams should have direct access to standards/protocols with detailed "how-to". These protocols should be read together in a meeting, showing everyone where every information is. This way, it is possible to ensure that any questions the field team has can be solved without internet access. Therefore, analysts must keep in mind that field communication is directly dependent on themselves rather than field teams. We are the ones who need to make it clear how we need the data to be.

Effective communication with field teams is crucial for resolving data discrepancies. Engage with team members to understand the context of data collection, which can clarify misunderstandings or errors. Regular check-ins and clear communication channels help ensure that teams adhere to data collection protocols and appreciate the importance of accuracy.

Data Involves comparing conflicting data points to determine the most accurate values. Use GIS software tools to merge datasets and systematically resolve discrepancies, and if necessary, re-collect data or apply statistical methods to ensure consistency and accuracy.

You will need to look at how your data usually looks and then find any outliers or discrepancies. If you find something that looks wrong, flag it in your data and contact the field staff. They have been to the sites and will know if what you are seeing is likely to be wrong. Once you have discussed with the field staff or the IT staff, then you will need to backup your data first in case you need to roll back later. Once you have done a backup, then you can make corrections and verify with your team.

The team will need to analyze the systems that they are using to collect data. Is there an issue with the server or with the collectors? How can this be fixed? Identify the weak point and then work together to find a better way forward. Once you have this, your data will be more accurate.

Quality Assurance is crucial before releasing data. It is recommended to have two environments, a public facing environment and an internal environment. This will make it so that you can edit in the internal environment and also share the correct and clean data in your public environment. Quality assurance is done in the internal environment. This can involve reviewing thousands or millions of data points, or even less than 10, depending on the scale of your operation. You will need to create bulk review processes and try your best to maintain them. Switching processes can be daunting so usually maintaining the process you have are the best choice. like the saying, "If it ain't broke, then don't fix it!"

QA is actually what makes your data reliable. There is a field of study called "Accuracy Assessment" and protocols such as ISO 19111 are highly recommended for that (though each country should have its own). This ensures knowledge about the data we are analysing, catching errors and raises credibility for our clients later on. It's a small effort with great outcomes.