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如何使用 ID 创建最佳的 world.topo.json 文件?

本文介绍了如何使用 ID 创建最佳的 world.topo.json 文件?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

鉴于世界各国的庞大形状文件.

鉴于我使用的是通过 npm install [email protected] 在本地安装的 [email protected].通过 npm install [email protected] 在本地安装.

鉴于我有许多国家/地区的 csv 数据，例如 

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

鉴于我想将该数据绑定到 Topojson+D3js 生成的 SVG.

因此，我想要一个具有权限属性的轻便而精确的 world-id.topojson 文件...以便通过匹配 ID 简化 CSV-SVG 数据投标.

所以，我选择:

#下载GADM卷曲\-L -C - 'http://biogeo.ucdavis.edu/data/gadm2.8/gadm28_levels.shp.zip'\-o ./gadm28_levels.shp.zip解压 -n ./gadm28_levels.shp.zip -d ./# 处理数据节点 ./node_modules/topojson/bin/topojson -q 1e4 \-p 名称=NAME_ENGL,iso=ISO,iso2=ISO2-o world-all.json \-- ./gadm28_adm0.shp

但它失败了，Aborted (core dumped).如何进行?

干净优雅

GADM 数据

#在本地安装topojson v.1npm 安装 [email protected]# 运行 topojson节点 --max_old_space_size=8000 ./node_modules/topojson/bin/topojson \-q 1e4 \-p 名称=NAME_ENGL,iso=ISO,iso2=ISO2 \-o world-id.json \-- 国家=./gadm28_adm0.shp

Shapefile 的数据是这样的:

{特性": {"对象 ID": 79,ID_0":79，"ISO": "FRA","NAME_ENGLI": "法国","NAME_ISO": "法国","NAME_FAO": "法国","NAME_LOCAL": "法国",NAME_OBSOL":空，NAME_VARIA":空，NAME_NONLA":空，"NAME_FRENC": "法国","NAME_SPANI": "弗朗西亚","NAME_RUSSI": "ФÑанÑиÑ","NAME_ARABI": "ÙرÙسا","NAME_CHINE": "æ³å½",WASPARTOF":空，包含":空，"SOVEREIGN": "法国","ISO2": "FR",WWW":空，"FIPS": "FR",伊森":250，"VALIDFR": "1944","VALIDTO": "现在",POP2000":59237668，SQKM":546728.875，POPSQKM":108.349258122，"UNREGION1": "西欧","UNREGION2": "欧洲",发展":2，独联体":0，过渡":0，经合组织":1，WBREGION":空，"WBINCOME": "高收入:经合组织","WBDEBT": "债务未分类","WBOTHER": "动车组","CEEAC": 0,CEMAC":0，"CEPLG": 0,"COMESA": 0,EAC":0，"西非经共体": 0,伊加德":0，国际奥委会":0，"MRU": 0,"SACU": 0,"UEMOA": 0,乌玛":0，"PALOP": 0,部分":0，"CACM": 0,"EurAsEC": 0,阿加迪尔":0，南盟":0，东盟":0，北美自由贸易协定":0，海湾合作委员会":0，"南航": 0,加勒比共同体":0，欧盟":1，可以":0，ACP":0，内陆":0，"AOSIS": 0,小岛屿发展中国家":0，岛屿":0，最不发达国家":0，"Shape_Leng": 130.51585694,形状_区域":64.5133204963}}

自然地球数据

  • 下载:1.3G
  • 输入:实际源只有 5M，不会因大小而崩溃.
  • 输出:优雅的world-id.json，579.9kb.
命令

# 下载 NaturalEarthData卷曲\-L -C - 'https://github.com/nvkelso/natural-earth-vector/archive/v4.0.0.zip' \-o ./ne.shp.zip解压 -n ./ne.shp.zip -d ./# 在本地安装 topojson v.1npm 安装 [email protected]# 运行 topojson节点 ./node_modules/topojson/bin/topojson -q 1e3 --bbox \-p 名称=管理员,iso2=WB_A2,iso3=WB_A3 \-o world-id.json \-- country=./natural-earth-vector-4.0.0/10m_culture/ne_10m_admin_0_countries.shp

注:NE v4.0 数据为:

{特性": {"scalerank": 0,"featurecla": "Admin-0 国家",标签等级":2，"SOVEREIGNT": "法国","SOV_A3": "FR1",ADM0_DIF":1，级别":2，"TYPE": "国家","ADMIN": "法国","ADM0_A3": "FRA","GEOU_DIF": 0,"GEOUNIT": "法国","GU_A3": "FRA","SU_DIF": 0,"SUBUNIT": "法国","SU_A3": "FRA","BRK_DIFF": 0,"NAME": "法国","NAME_LONG": "法国","BRK_A3": "FRA","BRK_NAME": "法国",BRK_GROUP":空，"ABBREV": "神父","邮政": "F","FORMAL_EN": "法兰西共和国",FORMAL_FR":空，"NAME_CIAWF": "法国",NOTE_ADM0":空，NOTE_BRK":空，"NAME_SORT": "法国",NAME_ALT":空，"MAPCOLOR7": 7,MAPCOLOR8":5，"MAPCOLOR9": 9,MAPCOLOR13":11，POP_EST":67106161，"POP_RANK": 16,GDP_MD_EST":2699000，POP_YEAR":2017，上次人口普查":-99，"GDP_YEAR": 2016,"经济": "1. 发达地区: G7","INCOME_GRP": "1. 高收入:经合组织",维基百科":-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": "仅包括法国本土(包括科西嘉)","ADM0_A3_IS": "FRA","ADM0_A3_US": "FRA",ADM0_A3_UN":-99，ADM0_A3_WB":-99，大陆":欧洲"，"REGION_UN": "欧洲","SUBREGION": "西欧","REGION_WB": "欧洲和中亚",NAME_LEN":6，"LONG_LEN": 6,"ABBREV_LEN": 3,小":-99，家庭部分":1，"MIN_ZOOM": 0,MIN_LABEL":1.7，MAX_LABEL":6.7}}

Given a massive shape file of world countries.

Given I'am using [email protected] installed locally via npm install [email protected].talled locally via npm install [email protected].

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.

解决方案

Currently via [email protected]. (version using [email protected] 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 [email protected]
# 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 [email protected]
# 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
  }
}

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09-16 08:14
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