TopoJSON – Understanding Differences with GeoJSON

geojsonjsontopojsontopology

This is a conceptual question.

What are the differences between these? and what are the pros and cons of these formats?

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According to Mike Bostock (and other contributors to the TopoJSON extension):

TopoJSON is an extension of GeoJSON that encodes topology. Rather than representing geometries discretely, geometries in TopoJSON files are stitched together from shared line segments called arcs. TopoJSON eliminates redundancy, offering much more compact representations of geometry than with GeoJSON; typical TopoJSON files are 80% smaller than their GeoJSON equivalents. In addition, TopoJSON facilitates applications that use topology, such as topology-preserving shape simplification, automatic map coloring, and cartograms.

One possible disadvantage of TopoJSON is that you need to implement support for converting the TopoJSON back into GeoJSON, for libraries that only work with GeoJSON.

Related Solutions

GeoJSON and JSON Merging – How to Join GeoJSON and JSON

i dont know where you want to merge but you can do it with python as following code.

merged = {
   firstObj : {"parent_area": null, "generation_high": 1, "all_names": {"default": ["OSM Default", "South Georgia"]}, "id": 14455, "codes": {"osm_rel": "1983629"}, "name": "South Georgia", "country": "G", "type_name": "OSM Administrative Boundary Level 3", "generation_low": 1, "country_name": "Global", "type": "O03"},    
   secondObj : { "type": "MultiPolygon", "coordinates": [ [ [ [ -37.652271, -54.065565 ], [ -37.649751, -54.066685 ], [ -37.644852, -54.065705 ], [ -37.644572, -54.064725 ], [ -37.652271, -54.065565 ] ] ], [ [ [ -38.297102, -53.993427 ], [ -38.295513, -53.992544 ], [ -38.294201, -53.992191 ], [ -38.294226, -53.991661 ], [ -38.293848, -53.991661 ] ] ] ] }
};

Code:

import json

merged = { 'firstObj ' : obj1, 'secondObj' : obj2 }
json.dumps(merged)

[GIS] How to avoid corrupt geometries when translating between TopoJSON and GeoJSON

The issue here is TopoJSON’s default quantization behavior; you need to increase the quantization precision if you want to preserve detail when zoomed in. Try -q 1e5 to increase the quantization factor from the default by 10, or -q 1e6 by 100.

The appropriate value of Q depends on the maximum effective size of your map and limited by the original precision of the geometry. Your map appears to have 11 zoom levels by powers of 2, so starting with the smallest size of 560×410 at zoom level 0, the effective size of the map at zoom level 10 is 573,440×419,840. If you want to maintain this precision, you will need Q = 1e6 (and a significantly larger TopoJSON file).

More details: the quantization factor Q determines the maximum number of differentiable points and defaults to 10,000. For best efficiency, Q should be a factor of 10 because digits are base-10 encoded in JSON. The default value is appropriate for displaying a map on a computer screen which typically has at most a resolution of 2,560×1,440 pixels, well under 10,000×10,000. (Q is an upper bound on differentiability; if points are not uniformly spaced, you may get fewer differentiable points.)

TopoJSON uses quantization to determine whether two points are coincident for the purpose of simplification. This is required because GeoJSON does not encode topology, and exact matches would be overly strict due to floating point error in GeoJSON coordinates. Also, quantization is a major factor in reducing the size of the TopoJSON encoding, especially in conjunction with the delta encoding.

Slightly related, here is an example of the Asia Lambert Conic Conformal projection used for Russia.

Best Answer

Related Solutions

GeoJSON and JSON Merging – How to Join GeoJSON and JSON

[GIS] How to avoid corrupt geometries when translating between TopoJSON and GeoJSON

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