How can you choose the best data structure for a GIS algorithm?

Managing Proprietary Formats in GIS Integration between CAD and GIS is common, emphasizing the challenges of proprietary formats like DWG. While DWG is rich in design data, it's not easily read by open-source GIS tools, and some DWG entities can be complex to interpret. This poses interoperability issues, complicating data sharing. As software evolves, proprietary formats risk becoming unreadable, threatening data preservation. Tools like FME and GDAL can bridge these gaps, but at a cost. Balancing these challenges against the benefits of proprietary formats is key. Embracing open standards, such as GeoJSON, ensures broader compatibility and long-lasting

Raster image file formats are also an important consideration when storing geospatial datasets. For example, the TIFF format allows for multiple layers of the same basic information to be stored in a single file, typically referred to as subfiles. This capacity to store layers of data in a single file makes it popular for use with satellite images, which are primarily in the form of single-band images that can be overlaid to form color-composite images. The JPEG format, on the other hand, takes advantage of lossy compression to generate an estimate of the original image and thus a smaller file size. While it cannot be used for "traditional" GIS applications, it still can be used in computer vision applications such as object detection.

I find that vector data is most useful for data that is distinct and specific or heterogenous. For example, if you are trying to indicate the location of schools, banks, utilities, etc, it would be best to make use of vector data. Then when looking to represent homogenous or continuous data, you can use raster, e.g. elevation, land use/land cover, etc. When I was involved in a school mapping project in Abuja, we used vector points to indicate school locations, however, when we sought to use them to create a best location analysis for where new schools could be located, we had to create a raster from the data we had. It was an interesting project.

Choosing the right data structure for GIS algorithms is a fusion of art and science. Begin with a clear understanding of the spatial data and operations. Study the algorithm's requirements and the real-world problems it aims to solve. Opt for data structures like Quadtree or R-tree that are spatially indexed, aiding in efficient querying and manipulation. Experiment, measure, and adapt. Embrace an iterative process, optimizing for performance, memory usage, and scalability. Remember, every choice today lays the foundation for the innovations of tomorrow. Stay curious, stay informed, and let your creativity in problem-solving lead the way.

Vector data, with its ability to represent discrete and irregular features while preserving geometric accuracy, shines in scenarios where complex spatial relationships are paramount. It's the go-to choice for tasks like mapping administrative boundaries, road networks, and river systems. On the other hand, raster data excels when dealing with continuous and regularly sampled phenomena, such as elevation models, temperature distributions, or land cover classifications. Its strength lies in its ability to perform quick arithmetic operations and overlay analyses, making it invaluable for tasks like slope analysis or combining various layers to derive new information.

For raster management it’s recommend to use image/raster pyramids. And for 3D tiles (3D mesh models) there is similar alternative method called LOD (Level of Detail). Mentioned quadtree is performing very well in image/raster catching and is my recommend option as well.

If one is looking to share a raster file over the web and wants to make sure that the raster loads efficiently in a client device it’s a good idea to break it into tiles and cache it, this way the server won’t have to query the data everytime the raster is being loaded or zoomed in or zoom out as server can now return the pre created tile/cache based on zoom level in which client device is trying to view the data.

Another important data structure in GIS is a spatial index, which is a way of organizing and accessing spatial data based on their location. A spatial index can speed up the performance of spatial queries and operations, such as finding the nearest feature, intersecting features, or clipping features. A spatial index can also reduce the memory usage and disk space required to store spatial data. There are different types of spatial indexes, such as quadtree, R-tree, or geohash. Each type has its own advantages and disadvantages, depending on the characteristics of the data and the algorithm. For example, a quadtree divides the space into four equal quadrants recursively, until each quadrant contains a certain.

Data structure and the desired calculations are always important. A major question to answer is if the process is one-off or is it indented to be automated. A one-off process has more flexibility in many cases and can move in and out of different programs and programming methods.

Common mistake in data formats is that users sacrifice content and metadata by choosing the less complex file formats to reduce file size for easier handling or faster loading. I recommend tiling the data first to reduce individual file size, before choosing files formats compressing content and information. Using tiled data into for example map sheets can improve performance without need of decreasing the information that files carry.

When talking about vector data format, a common file type is a Shapefile, or human-friendly GeoJSON. Those unfortunately can take a long time to parse in big scale. Newly available GeoParquet format allows for fast reading and even streaming in out-of-memory computations without file size limitations.

About data formats, when talking about Raster data, one have to consider the area covered and the resolution of data (meter/Pixel). Some format are more suitable in compressing vaste amount of data rather other. Also, consider tiling your Raster data in homogeneous regions. A well designed space/detail balance here play a huge role.

Generally, data formats are specific to GIS software and support an ecosystem of support intended by a company. What seems to be missing in our education programs is the concept that GIS is a system of programming that facilitates geospatial systems. For example, the Geospatial Data Abstraction Library (GDAL) is the bases for many open-source programs as well as software that has transitioned to a service model. GDAL started in 2000 and is still being added to and improved. GDAL continues to be the best source of transitioning between data formats and open-source programs continue to develop the capability.

One of the most overlooked issues in GIS data processing is the coordinate reference system (CRS) that each dataset makes use of. Make sure all datasets confirm to a uniform CRS so that each data layer properly overlaps each other.

A good understanding of data structures is required in the GIS domain. For path traversal, familiarity with graph data structures is essential. And when spatial searching is on your itinerary, acquaint yourself with quadtrees and k-d trees—they prove invaluable. Rather than reinventing the wheel, tap into the wealth of knowledge within existing GIS libraries and frameworks.

The choice of data structure for a GIS algorithm depends on the specific requirements of the project e.g. GIS datasets can be large, and memory and disk efficiency are important factors to consider. Look for data structures that optimize memory usage and disk access. For example, compressing data or using data structures designed for efficient storage, like B-trees or Z-order curves. Adopting data formats like Zarr, specifically for storing N-Dimensional arrays by dividing the arrays into chunks and compressing each chunk.

The best teacher is experience!!! The University where I teach had two methods of completing a Masters in Geography: a project or a thesis. They changed it to thesis only and there is no doubt that knowing the academic process is important. However, when I returned to alma mater to teach, it was a class based on specific GIS methods in support of flood risk management and emergency response. Today's GIS is not yesterdays, and it too has become specialized. At some point task organization will need to be addressed and integrated into a curriculum. Learning software does not make geospatial professionals - its more about solving geospatial problems.

The project should be considering memory and storage constraints. Some data structures are more memory-efficient but may have slower access times, while others may require more memory but offer faster retrieval. Evaluating the efficiency of data retrieval operations. Depending on algorithm's requirements, quick retrieval of individual data points or efficient range queries need to be checked.