How to create optimal world.topo.json file with IDs?

问题

Given a massive shape file of world countries.

Given I'am using Topojson@1.0 installed locally via npm install topojson@1.0. talled locally via npm install topojson@1.0.

Given I have csv data for many countries such as

FR,144145
EN,5643
DE,25667
ES,3567
US,83466
CN,34576
JA,69353

Given I want to bind that data to the Topojson+D3js generated SVG.

Thus I want a light yet precise world-id.topojson file with the rights properties... so to ease up the CSV-SVG data biding via matchings ids.

So, I go for :

# download GADM
curl \
    -L -C - 'http://biogeo.ucdavis.edu/data/gadm2.8/gadm28_levels.shp.zip' \
    -o ./gadm28_levels.shp.zip
unzip -n ./gadm28_levels.shp.zip -d ./
# Process data
node ./node_modules/topojson/bin/topojson -q 1e4 \
-p name=NAME_ENGL,iso=ISO,iso2=ISO2
-o world-all.json \
-- ./gadm28_adm0.shp

But it fails with Aborted (core dumped). How to proceed ?

EDIT: clean elegant world-id.json, 579.9kb, with iso-639-2, countrynames, and iso-639-3.

回答1:

Currently via Topojoson@1.0. (version using Topojson@3.0 welcome!)

Output (Natural Earth) : clean elegant world-id.json, 579.9kb, with iso-639-2, countrynames, and iso-639-3.

Properties filtering

Add -p to keep all properties and their values, use nothing to drop them all, and use -p ISO to transmit to your topojson the duo "ISO": "FRA". See Topojson v.1 API

What we want

Data sample / visual :

{
  "type": "MultiPolygon",
  "arcs": [
    [ [4347,4348,4349] ],
    [ [4350,4350,4351,4352,4353,4354] ],
    [ [4355,4356,4357,4358,4358,4358,4359,4360,4361,-4350,4362,4363,-3047,-1961,-1960,-598], [4364], [4365] ]
  ],
  "properties": {
    "name": "Italy",
    "iso2": "IT",
    "iso3": "ITA"
  }
},

GADM data

# Install topojson v.1 locally
npm install topojson@1.0
# Run topojson
node --max_old_space_size=8000 ./node_modules/topojson/bin/topojson \
     -q 1e4 \
     -p name=NAME_ENGL,iso=ISO,iso2=ISO2 \
     -o world-id.json \
     -- countries=./gadm28_adm0.shp

Shapefile's data is such :

{
  "properties": {
    "OBJECTID": 79,
    "ID_0": 79,
    "ISO": "FRA",
    "NAME_ENGLI": "France",
    "NAME_ISO": "FRANCE",
    "NAME_FAO": "France",
    "NAME_LOCAL": "France",
    "NAME_OBSOL": null,
    "NAME_VARIA": null,
    "NAME_NONLA": null,
    "NAME_FRENC": "France",
    "NAME_SPANI": "Francia",
    "NAME_RUSSI": "Ð¤ÑÐ°Ð½ÑÐ¸Ñ",
    "NAME_ARABI": "ÙØ±ÙØ³Ø§",
    "NAME_CHINE": "æ³å½",
    "WASPARTOF": null,
    "CONTAINS": null,
    "SOVEREIGN": "France",
    "ISO2": "FR",
    "WWW": null,
    "FIPS": "FR",
    "ISON": 250,
    "VALIDFR": "1944",
    "VALIDTO": "Present",
    "POP2000": 59237668,
    "SQKM": 546728.875,
    "POPSQKM": 108.349258122,
    "UNREGION1": "Western Europe",
    "UNREGION2": "Europe",
    "DEVELOPING": 2,
    "CIS": 0,
    "Transition": 0,
    "OECD": 1,
    "WBREGION": null,
    "WBINCOME": "High income: OECD",
    "WBDEBT": "Debt not classified",
    "WBOTHER": "EMU",
    "CEEAC": 0,
    "CEMAC": 0,
    "CEPLG": 0,
    "COMESA": 0,
    "EAC": 0,
    "ECOWAS": 0,
    "IGAD": 0,
    "IOC": 0,
    "MRU": 0,
    "SACU": 0,
    "UEMOA": 0,
    "UMA": 0,
    "PALOP": 0,
    "PARTA": 0,
    "CACM": 0,
    "EurAsEC": 0,
    "Agadir": 0,
    "SAARC": 0,
    "ASEAN": 0,
    "NAFTA": 0,
    "GCC": 0,
    "CSN": 0,
    "CARICOM": 0,
    "EU": 1,
    "CAN": 0,
    "ACP": 0,
    "Landlocked": 0,
    "AOSIS": 0,
    "SIDS": 0,
    "Islands": 0,
    "LDC": 0,
    "Shape_Leng": 130.51585694,
    "Shape_Area": 64.5133204963
  }
}

Natural Earth Data

Download : 1.3G
Input : actual source is just 5M and doesn't crash due to size.
Output : elegant world-id.json, 579.9kb.

Command

# download NaturalEarthData
curl \
    -L -C - 'https://github.com/nvkelso/natural-earth-vector/archive/v4.0.0.zip' \
    -o ./ne.shp.zip
unzip -n ./ne.shp.zip -d ./
# Install topojson v.1 locally
npm install topojson@1.0
# Run topojson
node ./node_modules/topojson/bin/topojson -q 1e3 --bbox \
     -p name=ADMIN,iso2=WB_A2,iso3=WB_A3 \
     -o world-id.json \
     -- countries=./natural-earth-vector-4.0.0/10m_cultural/ne_10m_admin_0_countries.shp

Note: NE v4.0 data is :

{
  "properties": {
    "scalerank": 0,
    "featurecla": "Admin-0 country",
    "LABELRANK": 2,
    "SOVEREIGNT": "France",
    "SOV_A3": "FR1",
    "ADM0_DIF": 1,
    "LEVEL": 2,
    "TYPE": "Country",
    "ADMIN": "France",
    "ADM0_A3": "FRA",
    "GEOU_DIF": 0,
    "GEOUNIT": "France",
    "GU_A3": "FRA",
    "SU_DIF": 0,
    "SUBUNIT": "France",
    "SU_A3": "FRA",
    "BRK_DIFF": 0,
    "NAME": "France",
    "NAME_LONG": "France",
    "BRK_A3": "FRA",
    "BRK_NAME": "France",
    "BRK_GROUP": null,
    "ABBREV": "Fr.",
    "POSTAL": "F",
    "FORMAL_EN": "French Republic",
    "FORMAL_FR": null,
    "NAME_CIAWF": "France",
    "NOTE_ADM0": null,
    "NOTE_BRK": null,
    "NAME_SORT": "France",
    "NAME_ALT": null,
    "MAPCOLOR7": 7,
    "MAPCOLOR8": 5,
    "MAPCOLOR9": 9,
    "MAPCOLOR13": 11,
    "POP_EST": 67106161,
    "POP_RANK": 16,
    "GDP_MD_EST": 2699000,
    "POP_YEAR": 2017,
    "LASTCENSUS": -99,
    "GDP_YEAR": 2016,
    "ECONOMY": "1. Developed region: G7",
    "INCOME_GRP": "1. High income: OECD",
    "WIKIPEDIA": -99,
    "FIPS_10_": "FR",
    "ISO_A2": "-99",
    "ISO_A3": "-99",
    "ISO_A3_EH": "-99",
    "ISO_N3": "250",
    "UN_A3": "250",
    "WB_A2": "FR",
    "WB_A3": "FRA",
    "WOE_ID": -90,
    "WOE_ID_EH": 23424819,
    "WOE_NOTE": "Includes only Metropolitan France (including Corsica)",
    "ADM0_A3_IS": "FRA",
    "ADM0_A3_US": "FRA",
    "ADM0_A3_UN": -99,
    "ADM0_A3_WB": -99,
    "CONTINENT": "Europe",
    "REGION_UN": "Europe",
    "SUBREGION": "Western Europe",
    "REGION_WB": "Europe & Central Asia",
    "NAME_LEN": 6,
    "LONG_LEN": 6,
    "ABBREV_LEN": 3,
    "TINY": -99,
    "HOMEPART": 1,
    "MIN_ZOOM": 0,
    "MIN_LABEL": 1.7,
    "MAX_LABEL": 6.7
  }
}

来源：https://stackoverflow.com/questions/49680635/how-to-create-optimal-world-topo-json-file-with-ids

标签

node.js

topojson