When organizations begin sharing data within their spatial data infrastructure (SDI), they find that certain geospatial features at the edge or boundary of their dataset do not match in location or attribute with other shared data. While some automated tools such as conflation can be used for fixing sliver, gap or overlap issues in the data, human decision-making is required to obtain the best resulting data for use in other applications. Read this blog to see how collaboration can be used to review and align GIS data between organizations.
I recently attended a workshop where the facilitator encouraged participants to use a cloud-based note-sharing application. The application was very intuitive, and after practising for a few minutes, the participants were able to use the application to share each other’s ideas. So, it wasn’t long before these individuals started collaborating within the group using their digital text notes. In the end, everyone had access to all the notes and ideas from the session. I found that this form of text-based collaboration worked well for improving efficiency and creating results in a workshop setting.
This experience got me thinking about how a group of people could collaborate online using maps. There are often situations where several individuals, groups, organizations or governments need to agree on the location and attributes of certain features. Specifically, governments are often concerned about the location of boundaries such as borders, service areas, census, election and zoning. Let’s look at a use case where online collaboration between government agencies using maps is improving the efficiency of the process of coming to an agreeable decision by all groups, thus creating more interoperable data.
A prototypical situation is where two neighbouring municipalities need to combine their geospatial data to make a connected road network for transportation, emergency services and other uses. Often when two independent organizations collect data separately, there ends up being disconnected feature segments at the edges or boundaries. This break needs to be fixed, especially for roads as a connected network of segments is required for routing and other applications. Therefore, the two neighbouring municipalities need to collaborate to create a seamless (continuous) road network across the boundary.
An example of road centreline misalignment between two municipalities as shown in the white circle. The road in municipality A (blue line) and the municipality B (green line) at the municipal boundary (red line) needs to be aligned correctly to show a continuous road network.
ArcGIS supports distributed collaboration, where users can connect and integrate their GIS with a number of participants, thus enabling each participant to organize and share their content. Distributed collaboration (or simply, collaboration) is based on a foundation of trust and driven by common goals or initiatives, such as a seamless road network across boundaries. Once a trusted collaboration pattern is established, users can then extend their GIS data to the group of participants to resolve any data inconsistencies.
Both municipalities need to use collaboration to decide on the exact location of a tie point (yellow star). Using collaboration, the two municipalities can agree on the location of the yellow tie point and then adjust their road centrelines accordingly.
If you need to share your GIS content across your organization or with other external organizations, how do you accomplish it? There are different options available for sharing and collaborating in both ArcGIS Online and ArcGIS Enterprise. This technical webinar shows how to take advantage of them. Find out how to securely set up and manage the different collaboration options regardless of the kind of Esri technology being used.
A screenshot from the webinar demonstrates sharing between two ArcGIS organizations in a secure and trusted collaboration environment.
When geospatial content is produced independently by different groups or organizations, spatial or attribute differences between the datasets often need to be resolved at the boundaries. Conflation tools can help users reconcile data from multiple sources, but conflation rules follow a specific methodology to align the features, and the results may not always be 100% accurate. Collaboration between the organizations on these discontinuities will result in creating the most accurate spatial data. Collaboration is an important tool in the web GIS practitioner’s toolbox that allows cooperative solutions on data differences between organizations.