An Evaluation of Population Estimates in Florida: April 1, 2000 View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-02

AUTHORS

Stanley K. Smith, Scott Cody

ABSTRACT

The housing unit (HU) method is the most commonly used method for making small-area population estimates in the United States. These estimates are used for a wide variety of budgeting, planning, and analytical purposes. Given their importance, periodic evaluations of their accuracy are essential. In this article, we evaluate the accuracy of a set of HU population estimates for counties and subcounty areas in Florida, as of April 1, 2000. We investigate the influence of differences in population size and growth rate on estimation errors; compare the accuracy of several alternative techniques for estimating each of the major components of the HU method; compare the accuracy of 2000 estimates with that of estimates produced in 1980 and 1990; compare the accuracy of HU population estimates with that of estimates derived from other estimation methods; consider the role of professional judgment and the use of averaging in the construction of population estimates; and explore the impact of controlling one set of estimates to another. Our results confirm a number of findings that have been reported before and provide empirical evidence on several issues that have received little attention in the literature. We conclude with several observations regarding future directions in population estimation research. More... »

PAGES

1-24

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/b:popu.0000019918.24143.c9

DOI

http://dx.doi.org/10.1023/b:popu.0000019918.24143.c9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004872019


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1403", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Econometrics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "name": [
            "University of Florida, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Smith", 
        "givenName": "Stanley K.", 
        "id": "sg:person.07607321731.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07607321731.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "University of Florida, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cody", 
        "givenName": "Scott", 
        "id": "sg:person.01044064120.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044064120.91"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.2307/2061251", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014666635", 
          "https://doi.org/10.2307/2061251"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1016537822148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020758577", 
          "https://doi.org/10.1023/a:1016537822148"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1353/dem.2002.0040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025702030", 
          "https://doi.org/10.1353/dem.2002.0040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3828/twpr.18.2.ul31w6q4447g120r", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026712220"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.2307/2061106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027807537", 
          "https://doi.org/10.2307/2061106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01944369408975574", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037332982"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03208591", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044853045", 
          "https://doi.org/10.1007/bf03208591"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-0-306-47630-3_19", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049677424", 
          "https://doi.org/10.1007/978-0-306-47630-3_19"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01944367708977786", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053030078"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1984.10478042", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058302968"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1986.10478272", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058303249"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2808054", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070096145"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2983171", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070160945"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2307/2983171", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1070160945"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004-02", 
    "datePublishedReg": "2004-02-01", 
    "description": "The housing unit (HU) method is the most commonly used method for making small-area population estimates in the United States. These estimates are used for a wide variety of budgeting, planning, and analytical purposes. Given their importance, periodic evaluations of their accuracy are essential. In this article, we evaluate the accuracy of a set of HU population estimates for counties and subcounty areas in Florida, as of April 1, 2000. We investigate the influence of differences in population size and growth rate on estimation errors; compare the accuracy of several alternative techniques for estimating each of the major components of the HU method; compare the accuracy of 2000 estimates with that of estimates produced in 1980 and 1990; compare the accuracy of HU population estimates with that of estimates derived from other estimation methods; consider the role of professional judgment and the use of averaging in the construction of population estimates; and explore the impact of controlling one set of estimates to another. Our results confirm a number of findings that have been reported before and provide empirical evidence on several issues that have received little attention in the literature. We conclude with several observations regarding future directions in population estimation research.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1023/b:popu.0000019918.24143.c9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1094213", 
        "issn": [
          "0167-5923", 
          "1573-7829"
        ], 
        "name": "Population Research and Policy Review", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "23"
      }
    ], 
    "name": "An Evaluation of Population Estimates in Florida: April 1, 2000", 
    "pagination": "1-24", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "17370f0467a0f16e1d95075e876003f05d7430e50f8d1f54861ca80266d96c02"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1023/b:popu.0000019918.24143.c9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1004872019"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1023/b:popu.0000019918.24143.c9", 
      "https://app.dimensions.ai/details/publication/pub.1004872019"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T15:49", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8664_00000503.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1023%2FB%3APOPU.0000019918.24143.c9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1023/b:popu.0000019918.24143.c9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1023/b:popu.0000019918.24143.c9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/b:popu.0000019918.24143.c9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1023/b:popu.0000019918.24143.c9'


 

This table displays all metadata directly associated to this object as RDF triples.

114 TRIPLES      21 PREDICATES      40 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1023/b:popu.0000019918.24143.c9 schema:about anzsrc-for:14
2 anzsrc-for:1403
3 schema:author N6d8bae5f49c34a5085f75150ea15e535
4 schema:citation sg:pub.10.1007/978-0-306-47630-3_19
5 sg:pub.10.1007/bf03208591
6 sg:pub.10.1023/a:1016537822148
7 sg:pub.10.1353/dem.2002.0040
8 sg:pub.10.2307/2061106
9 sg:pub.10.2307/2061251
10 https://doi.org/10.1080/01621459.1984.10478042
11 https://doi.org/10.1080/01621459.1986.10478272
12 https://doi.org/10.1080/01944367708977786
13 https://doi.org/10.1080/01944369408975574
14 https://doi.org/10.2307/2808054
15 https://doi.org/10.2307/2983171
16 https://doi.org/10.3828/twpr.18.2.ul31w6q4447g120r
17 schema:datePublished 2004-02
18 schema:datePublishedReg 2004-02-01
19 schema:description The housing unit (HU) method is the most commonly used method for making small-area population estimates in the United States. These estimates are used for a wide variety of budgeting, planning, and analytical purposes. Given their importance, periodic evaluations of their accuracy are essential. In this article, we evaluate the accuracy of a set of HU population estimates for counties and subcounty areas in Florida, as of April 1, 2000. We investigate the influence of differences in population size and growth rate on estimation errors; compare the accuracy of several alternative techniques for estimating each of the major components of the HU method; compare the accuracy of 2000 estimates with that of estimates produced in 1980 and 1990; compare the accuracy of HU population estimates with that of estimates derived from other estimation methods; consider the role of professional judgment and the use of averaging in the construction of population estimates; and explore the impact of controlling one set of estimates to another. Our results confirm a number of findings that have been reported before and provide empirical evidence on several issues that have received little attention in the literature. We conclude with several observations regarding future directions in population estimation research.
20 schema:genre research_article
21 schema:inLanguage en
22 schema:isAccessibleForFree false
23 schema:isPartOf N76dae20d62014d7a9909eec29eb817dd
24 Nf29d6eeff6424d548c2a8c13ae8dc8fb
25 sg:journal.1094213
26 schema:name An Evaluation of Population Estimates in Florida: April 1, 2000
27 schema:pagination 1-24
28 schema:productId N3f43f6fdb67648eea89e2bf8f03485de
29 N3fd376a10bd74bd19ce20d5771f36eb8
30 N83f23b4e80134725a7d21707349f26ab
31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004872019
32 https://doi.org/10.1023/b:popu.0000019918.24143.c9
33 schema:sdDatePublished 2019-04-10T15:49
34 schema:sdLicense https://scigraph.springernature.com/explorer/license/
35 schema:sdPublisher Nb007b0d435c24d7e9f0a6517111f12e8
36 schema:url http://link.springer.com/10.1023%2FB%3APOPU.0000019918.24143.c9
37 sgo:license sg:explorer/license/
38 sgo:sdDataset articles
39 rdf:type schema:ScholarlyArticle
40 N1130aafe24b04440aaa6f807e7a13f26 rdf:first sg:person.01044064120.91
41 rdf:rest rdf:nil
42 N3f43f6fdb67648eea89e2bf8f03485de schema:name dimensions_id
43 schema:value pub.1004872019
44 rdf:type schema:PropertyValue
45 N3fd376a10bd74bd19ce20d5771f36eb8 schema:name readcube_id
46 schema:value 17370f0467a0f16e1d95075e876003f05d7430e50f8d1f54861ca80266d96c02
47 rdf:type schema:PropertyValue
48 N6d8bae5f49c34a5085f75150ea15e535 rdf:first sg:person.07607321731.48
49 rdf:rest N1130aafe24b04440aaa6f807e7a13f26
50 N76dae20d62014d7a9909eec29eb817dd schema:volumeNumber 23
51 rdf:type schema:PublicationVolume
52 N83f23b4e80134725a7d21707349f26ab schema:name doi
53 schema:value 10.1023/b:popu.0000019918.24143.c9
54 rdf:type schema:PropertyValue
55 Nb007b0d435c24d7e9f0a6517111f12e8 schema:name Springer Nature - SN SciGraph project
56 rdf:type schema:Organization
57 Ncd3436593651461d93401b6ad697a410 schema:name University of Florida, Australia
58 rdf:type schema:Organization
59 Nf16ead937db5476c8c6ae87362a56302 schema:name University of Florida, Australia
60 rdf:type schema:Organization
61 Nf29d6eeff6424d548c2a8c13ae8dc8fb schema:issueNumber 1
62 rdf:type schema:PublicationIssue
63 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
64 schema:name Economics
65 rdf:type schema:DefinedTerm
66 anzsrc-for:1403 schema:inDefinedTermSet anzsrc-for:
67 schema:name Econometrics
68 rdf:type schema:DefinedTerm
69 sg:journal.1094213 schema:issn 0167-5923
70 1573-7829
71 schema:name Population Research and Policy Review
72 rdf:type schema:Periodical
73 sg:person.01044064120.91 schema:affiliation Ncd3436593651461d93401b6ad697a410
74 schema:familyName Cody
75 schema:givenName Scott
76 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01044064120.91
77 rdf:type schema:Person
78 sg:person.07607321731.48 schema:affiliation Nf16ead937db5476c8c6ae87362a56302
79 schema:familyName Smith
80 schema:givenName Stanley K.
81 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07607321731.48
82 rdf:type schema:Person
83 sg:pub.10.1007/978-0-306-47630-3_19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049677424
84 https://doi.org/10.1007/978-0-306-47630-3_19
85 rdf:type schema:CreativeWork
86 sg:pub.10.1007/bf03208591 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044853045
87 https://doi.org/10.1007/bf03208591
88 rdf:type schema:CreativeWork
89 sg:pub.10.1023/a:1016537822148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020758577
90 https://doi.org/10.1023/a:1016537822148
91 rdf:type schema:CreativeWork
92 sg:pub.10.1353/dem.2002.0040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025702030
93 https://doi.org/10.1353/dem.2002.0040
94 rdf:type schema:CreativeWork
95 sg:pub.10.2307/2061106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027807537
96 https://doi.org/10.2307/2061106
97 rdf:type schema:CreativeWork
98 sg:pub.10.2307/2061251 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014666635
99 https://doi.org/10.2307/2061251
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1080/01621459.1984.10478042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058302968
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1080/01621459.1986.10478272 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303249
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1080/01944367708977786 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053030078
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1080/01944369408975574 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037332982
108 rdf:type schema:CreativeWork
109 https://doi.org/10.2307/2808054 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070096145
110 rdf:type schema:CreativeWork
111 https://doi.org/10.2307/2983171 schema:sameAs https://app.dimensions.ai/details/publication/pub.1070160945
112 rdf:type schema:CreativeWork
113 https://doi.org/10.3828/twpr.18.2.ul31w6q4447g120r schema:sameAs https://app.dimensions.ai/details/publication/pub.1026712220
114 rdf:type schema:CreativeWork
 




Preview window. Press ESC to close (or click here)


...