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

Question

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.

Answer 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": "ФÑанÑиÑ",
"NAME_ARABI": "ÙرÙسا",
"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
}
}