Spatial Data Structures for Better Map Interactions

Post by rdtsc » Fri, Mar 28, 2014, 1:26 AM UTC

jandrewrogers wrote: post #18

rdtsc wrote: post #14

Earlier quoted context omitted.

> And the most advanced algorithm family for indexing point-like data is not in the literature at all. So what is it? Is it patented, secret(classified)? And how do you know it is the most advanced without a peer review? BTW "adaptive spatial sieves" on Google search produces exactly 0 results. Is there another name for it. Spatial indexing is interesting and complex but not exactly rocket science, there have been a…

What makes spatial indexing different than other areas of computer science is that its fundamental operands are interval types (like hypercubes). Almost all other areas of computer science focus on solutions that only work on point-like data and so many idioms and intuitions computer scientists use are not actually valid when dealing with spatial representations. Data types that require no less than two integers to d…

Post by poiuytrewq88 » Fri, Apr 04, 2014, 11:56 AM UTC

jandrewrogers wrote: post #11

ryandrake wrote: post #3

Spatial data structures are great, and heavily used in all kinds of mapping applications. Most of the systems I've worked with used the more constrained quadtree structure, where the map is divided into uniformly-sized tiles, and a level-of-detail hierarchy is built. This is good for raster data, where each tile is a picture and the whole world is covered by tiles. The R-tree has great advantages when the complexity…

The spatial data structure literature is quite incomplete, and there is a lot of confusion about what to use when. Many of the issues with spatial indexing performance at companies I go into is that they are doing it wrong, not that they necessarily have a fundamental problem. It should be pointed out that R-family data structures should only be used for data sets that are (1) small and (2) relatively static. They sc…