All Geospatial projects contain an element of data collection, storage and preparation. This is a vital stage in project execution that has to be dealt with carefully and correctly as it would lead to loss of time and resources. Depending on the project, the Spatialite database is one of the best databases used to store spatial for analysis.
Participatory mapping involves a set of techniques and approaches. These combine the tools of modern cartography with participatory methods to represent the spatial knowledge of communities or groups. The projects involve large numbers of data collectors and digitizers.
Often, the data dealt with cover wider geographical areas and usually take time to complete the tasks. In cases of decentralized working setup, numerous errors are caused between groups leading to the need to allocate more resources and time to fix these errors.
The need for more resources leads to stalling or/and ending the project completely. Many participatory mapping projects end up this way. This might lead to chaos as the blame game will shift to project managers or users for funds embezzlement. I won’t dwell much on this as it’s a story for another day. Due to such issues, better and smarter solutions have been developed for use freely and openly. Different work-flow and tools are used in different projects depending on a number of factors.
In this article, I focus on spatial data manipulation, cleaning and storage. In a real-world project scenario, on community projects, the community members are trained on various techniques and tools so as to empower them to carry out the project within their areas or locality. As always attested to, the community’s members know their locality better. With this in mind, for example, the groups or teams are trained on data collection methods, data cleaning and preparation and finally map-making techniques. With such an approach, there is ensured continuity of the project and motivation among the participants to map their resources or areas even better.
This, therefore, brings in the need to manage data collected by the members, and keep a record of the different versions of data and final outputs from work. This process is continuously leading to the need for a versatile and robust system of working that will ensure the project objectives are achieved. Below is an illustration of a working environment, which represents most working groups and agencies involved in community mapping.
The diagram above illustrates a working environment where the project team (Digitizers, managers, supervisors, donors etc) can share a database and access different types of data depending on their levels and requirements. Most community mapping projects work on and produce freely accessible data for all the stakeholders involved in the project. In this case, the proposal for the SpatiaLite database doesn’t consider data security as one of the key factors at work. Connection to the database can either be Wi-Fi or LAN depending on the size of the dataset being accessed. Of course, users have preferences at work.
Why the SpatiaLite database?
- Support by most, if not all, software that exists in Geospatial (Interoperable)
- It’s OGC-compliant.
- The whole database is simply a file enabling sharing, and transferability and no configuration is required.
- It’s versatile, allowing large numbers of community users to connect simultaneously.
Participatory/community mapping involves quite a big number of digitizers and players. Data acquired from different teams differ in different aspects such as accuracy, correctness and completeness. These factors determine the quality of the data which is a huge concern in these projects. To ensure data quality, supervisors can review all data worked on by the digitizers (As a way of marking the work) and flag the errors that may exist in the dataset.
How is it done?
- The supervisor accesses the same layer worked on by a digitizer from the database and checks it through, highlighting the errors in the data (templates and forms are customized for this in the layer e.g. QGIS). The errors can have different flags depending on the type of error
- The digitizer is notified of the corrections, accesses the same layer, finds all the errors and corrects them
- The supervisor is notified of the digitizer’s corrections and counter-checks the changes. If all the corrections were made correctly, the layer is flagged as good or complete or finalized. Choose your words. If not all are correct, the supervisor marks the errors again for the digitizer to correct.
- This process is continued until an ideal version or quality of the data is achieved. SpatiaLite has proven to be a key tool in community mapping and continues to introduce more functionality to enhance interaction with spatial data and other workflows.
For more details, watch the SpatiaLite Tutorial
What’s your experience with SpatiaLite database?