[Answer]-Django loaddata: YAML fixtures + GeoDjango MultiPolygon

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GEOS is looking for WKT (Well Known Text) format, which is close to, but not the same as, the geoJSON/YAML format above.

The practical solution was to use geomet to transform the JSON input into WKT, which could then comfortably reside within the YAML file… fun with brackets varieties!

So then the successful format looks like this:

- fields: {external_id: toluca-lake, name: Toluca Lake, region: 9, shape: 'MULTIPOLYGON
      (((-118.357158 34.164806, -118.357154 34.163097, -118.357151 34.161287, -118.356036
      34.161287, -118.354978 34.161288, -118.354682 34.161288, -118.354020 34.161288,
      -118.353103 34.161289, -118.353034 34.161128, -118.352925 34.160873, -118.352156
      34.159076, -118.352138 34.159033, -118.351812 34.158271, -118.351554 34.157668,
      -118.351235 34.156925, -118.350751 34.155794, -118.350196 34.154497, -118.349988
      34.154012, -118.349958 34.153941, -118.349830 34.153812, -118.349756 34.153629,
      -118.349673 34.153425, -118.349643 34.153350, -118.349664 34.153256, -118.349216
      34.152209, -118.348450 34.150419, -118.348067 34.149523, -118.347680 34.148618,
      -118.347555 34.148327, -118.347308 34.147748, -118.346800 34.146562, -118.346767
      34.146485, -118.346624 34.146151, -118.346446 34.145735, -118.346430 34.145696,
      -118.345949 34.144573, -118.345903 34.144218, -118.345691 34.142572, -118.345678
      34.142466, -118.345665 34.142367, -118.345665 34.142367, -118.345698 34.142356,
      -118.346425 34.142207, -118.346907 34.142174, -118.347168 34.142177, -118.347168
      34.142177, -118.347522 34.142180, -118.348435 34.142262, -118.351608 34.142887,
      -118.352051 34.142964, -118.354160 34.143211, -118.354603 34.143227, -118.357641
      34.143337, -118.357783 34.143342, -118.357967 34.143348, -118.358339 34.143269,
      -118.358930 34.143143, -118.359082 34.143111, -118.359198 34.143088, -118.361136
      34.142712, -118.361258 34.142688, -118.361267 34.142686, -118.361264 34.142680,
      -118.361340 34.142669, -118.361411 34.142907, -118.361711 34.143307, -118.362211
      34.144107, -118.362911 34.145107, -118.363350 34.145900, -118.363863 34.146805,
      -118.364611 34.148106, -118.365011 34.148806, -118.365312 34.149206, -118.365512
      34.149606, -118.366012 34.150406, -118.366612 34.151206, -118.367112 34.152206,
      -118.367609 34.153023, -118.367885 34.153506, -118.368500 34.154522, -118.368612
      34.154706, -118.369145 34.155691, -118.369652 34.156644, -118.370237 34.157613,
      -118.369112 34.157606, -118.368112 34.157606, -118.367012 34.157606, -118.365929
      34.157604, -118.364912 34.157606, -118.363793 34.157613, -118.362712 34.157606,
      -118.361601 34.157613, -118.361623 34.159427, -118.361612 34.161206, -118.361612
      34.163106, -118.361612 34.164806, -118.360512 34.164806, -118.359412 34.164806,
      -118.358211 34.164806, -118.357211 34.164806, -118.357158 34.164806)))'}
  model: geo.neighborhood
👤Ben

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