为什么我不能使用 R 包 acs 从 SF1 获得国家和 ZCTA 级别的估计?
Why can't I get national and ZCTA-level estimates from SF1 using R package acs?
以下作品:
acs::acs.fetch(dataset = "acs",
endyear = 2015,
span = 5,
geography = acs::geo.make(zip = "*"),
variable = "B01001_001")
这也是:
acs::acs.fetch(dataset = "sf1",
endyear = 2010,
span = 0,
geography = acs::geo.make(state = "*"),
variable = "PCT0120001")
请向我解释为什么以下内容不起作用,因为这不是因为人口普查 API 没有可用的邮政编码级别估计值。我是否需要以不同方式指定地理区域才能从 sf1 获得国家和 ZCTA 级别的估计值,而不是从人口普查中的 acs5 获得国家级和 ZCTA 级估计值 API?
acs::acs.fetch(dataset = "sf1",
endyear = 2010,
span = 0,
geography = acs::geo.make(zip = "*"),
variable = "PCT0120001")
# Error in file(file, "rt") : cannot open the connection
# In addition: Warning message:
# No data found at:
# http://api.census.gov/data/2010/sf1?key=2dd03c4048ca2edb8463d8c0bbdc09c5eb3b4013&get=PCT0120001,NAME&for=zip+code+tabulation+area:*
acs::acs.fetch(dataset = "sf1",
endyear = 2010,
span = 0,
geography = acs::geo.make(us = "*"),
variable = "PCT0120001")
# Error in file(file, "rt") : cannot open the connection
# In addition: Warning message:
# No data found at:
# http://api.census.gov/data/2010/sf1?key=2dd03c4048ca2edb8463d8c0bbdc09c5eb3b4013&get=PCT0120001,NAME&for=us:*
这似乎是 2010 年十年一次的人口普查的局限性 API - 无法通过 API 获得整个美国的数据,ZCTA 数据只能按州获得。参见 http://api.census.gov/data/2010/sf1/geography.html。
您可以使用 totalcensus
package 下载摘要文件并提取每个邮政编码中的数据。数据下载到您自己的计算机上,因此对数据的访问不受人口普查的限制API。
library(totalcensus)
aaa <- read_decennial(
year = 2010,
states = "US",
table_contents = "PCT0120001",
geo_headers = "ZCTA5",
summary_level = "860"
)
print(aaa)
# lon lat ZCTA5 state population PCT0120001 GEOCOMP SUMLEV
# 1: -66.74996 18.18056 00601 NA 18570 18570 all 860
# 2: -67.17613 18.36227 00602 NA 41520 41520 all 860
# 3: -67.11989 18.45518 00603 NA 54689 54689 all 860
# 4: -66.93291 18.15835 00606 NA 6615 6615 all 860
# 5: -67.12587 18.29096 00610 NA 29016 29016 all 860
# ---
# 33116: -130.04103 56.00232 99923 NA 87 87 all 860
# 33117: -132.94593 55.55020 99925 NA 819 819 all 860
# 33118: -131.47074 55.13807 99926 NA 1460 1460 all 860
# 33119: -133.45792 56.23906 99927 NA 94 94 all 860
# 33120: -131.60683 56.41383 99929 NA 2338 2338 all 860
以下作品:
acs::acs.fetch(dataset = "acs",
endyear = 2015,
span = 5,
geography = acs::geo.make(zip = "*"),
variable = "B01001_001")
这也是:
acs::acs.fetch(dataset = "sf1",
endyear = 2010,
span = 0,
geography = acs::geo.make(state = "*"),
variable = "PCT0120001")
请向我解释为什么以下内容不起作用,因为这不是因为人口普查 API 没有可用的邮政编码级别估计值。我是否需要以不同方式指定地理区域才能从 sf1 获得国家和 ZCTA 级别的估计值,而不是从人口普查中的 acs5 获得国家级和 ZCTA 级估计值 API?
acs::acs.fetch(dataset = "sf1",
endyear = 2010,
span = 0,
geography = acs::geo.make(zip = "*"),
variable = "PCT0120001")
# Error in file(file, "rt") : cannot open the connection
# In addition: Warning message:
# No data found at:
# http://api.census.gov/data/2010/sf1?key=2dd03c4048ca2edb8463d8c0bbdc09c5eb3b4013&get=PCT0120001,NAME&for=zip+code+tabulation+area:*
acs::acs.fetch(dataset = "sf1",
endyear = 2010,
span = 0,
geography = acs::geo.make(us = "*"),
variable = "PCT0120001")
# Error in file(file, "rt") : cannot open the connection
# In addition: Warning message:
# No data found at:
# http://api.census.gov/data/2010/sf1?key=2dd03c4048ca2edb8463d8c0bbdc09c5eb3b4013&get=PCT0120001,NAME&for=us:*
这似乎是 2010 年十年一次的人口普查的局限性 API - 无法通过 API 获得整个美国的数据,ZCTA 数据只能按州获得。参见 http://api.census.gov/data/2010/sf1/geography.html。
您可以使用 totalcensus
package 下载摘要文件并提取每个邮政编码中的数据。数据下载到您自己的计算机上,因此对数据的访问不受人口普查的限制API。
library(totalcensus)
aaa <- read_decennial(
year = 2010,
states = "US",
table_contents = "PCT0120001",
geo_headers = "ZCTA5",
summary_level = "860"
)
print(aaa)
# lon lat ZCTA5 state population PCT0120001 GEOCOMP SUMLEV
# 1: -66.74996 18.18056 00601 NA 18570 18570 all 860
# 2: -67.17613 18.36227 00602 NA 41520 41520 all 860
# 3: -67.11989 18.45518 00603 NA 54689 54689 all 860
# 4: -66.93291 18.15835 00606 NA 6615 6615 all 860
# 5: -67.12587 18.29096 00610 NA 29016 29016 all 860
# ---
# 33116: -130.04103 56.00232 99923 NA 87 87 all 860
# 33117: -132.94593 55.55020 99925 NA 819 819 all 860
# 33118: -131.47074 55.13807 99926 NA 1460 1460 all 860
# 33119: -133.45792 56.23906 99927 NA 94 94 all 860
# 33120: -131.60683 56.41383 99929 NA 2338 2338 all 860