Urban mapping in Dar es Salaam using Angle-Based Joint and Individual Variation Explained Article Swipe
YOU?
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· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/jrsssc/qlaf043
Mapping deprivation in urban areas is important, for example, for identifying areas of greatest need and planning interventions. Traditional ways of obtaining deprivation estimates are based on either census or household survey data, which in many areas is unavailable or difficult to collect. However, there has been a huge rise in the amount of new, nontraditional forms of data, such as satellite imagery and cell-phone call-record data, which may contain information useful for identifying deprivation. We use Angle-Based Joint and Individual Variation Explained (AJIVE) to jointly model satellite imagery data, cell-phone data, and survey data for the city of Dar es Salaam, Tanzania. We first identify interpretable low-dimensional structure from the imagery and cell-phone data, and find that we can use these to identify deprivation. We then consider what is gained from further incorporating the more traditional and costly survey data. We also introduce a scalar measure of deprivation as a response variable to be predicted, and consider various approaches to multiview regression, including using AJIVE scores as predictors.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jrsssc/qlaf043
- https://academic.oup.com/jrsssc/advance-article-pdf/doi/10.1093/jrsssc/qlaf043/64070787/qlaf043.pdf
- OA Status
- hybrid
- References
- 11
- Related Works
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- OpenAlex ID
- https://openalex.org/W4413348182
Raw OpenAlex JSON
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https://openalex.org/W4413348182Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jrsssc/qlaf043Digital Object Identifier
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Urban mapping in Dar es Salaam using Angle-Based Joint and Individual Variation ExplainedWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-19Full publication date if available
- Authors
-
Rachel Carrington, Ian L. Dryden, Madeleine Ellis, James Goulding, Simon Preston, David SirlList of authors in order
- Landing page
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https://doi.org/10.1093/jrsssc/qlaf043Publisher landing page
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https://academic.oup.com/jrsssc/advance-article-pdf/doi/10.1093/jrsssc/qlaf043/64070787/qlaf043.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://academic.oup.com/jrsssc/advance-article-pdf/doi/10.1093/jrsssc/qlaf043/64070787/qlaf043.pdfDirect OA link when available
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Dar es salaam, Variation (astronomy), Geography, Joint (building), Tanzania, Engineering, Environmental planning, Civil engineering, Physics, AstrophysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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11Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.find | 117 |
| abstract_inverted_index.from | 110, 132 |
| abstract_inverted_index.huge | 49 |
| abstract_inverted_index.many | 36 |
| abstract_inverted_index.more | 136 |
| abstract_inverted_index.need | 15 |
| abstract_inverted_index.new, | 55 |
| abstract_inverted_index.rise | 50 |
| abstract_inverted_index.such | 60 |
| abstract_inverted_index.that | 118 |
| abstract_inverted_index.then | 127 |
| abstract_inverted_index.ways | 20 |
| abstract_inverted_index.what | 129 |
| abstract_inverted_index.AJIVE | 166 |
| abstract_inverted_index.Joint | 79 |
| abstract_inverted_index.areas | 5, 12, 37 |
| abstract_inverted_index.based | 26 |
| abstract_inverted_index.data, | 33, 59, 67, 90, 92, 115 |
| abstract_inverted_index.data. | 141 |
| abstract_inverted_index.first | 105 |
| abstract_inverted_index.forms | 57 |
| abstract_inverted_index.model | 87 |
| abstract_inverted_index.there | 45 |
| abstract_inverted_index.these | 122 |
| abstract_inverted_index.urban | 4 |
| abstract_inverted_index.using | 165 |
| abstract_inverted_index.which | 34, 68 |
| abstract_inverted_index.amount | 53 |
| abstract_inverted_index.census | 29 |
| abstract_inverted_index.costly | 139 |
| abstract_inverted_index.either | 28 |
| abstract_inverted_index.gained | 131 |
| abstract_inverted_index.scalar | 146 |
| abstract_inverted_index.scores | 167 |
| abstract_inverted_index.survey | 32, 94, 140 |
| abstract_inverted_index.useful | 72 |
| abstract_inverted_index.(AJIVE) | 84 |
| abstract_inverted_index.Mapping | 1 |
| abstract_inverted_index.Salaam, | 102 |
| abstract_inverted_index.contain | 70 |
| abstract_inverted_index.further | 133 |
| abstract_inverted_index.imagery | 63, 89, 112 |
| abstract_inverted_index.jointly | 86 |
| abstract_inverted_index.measure | 147 |
| abstract_inverted_index.various | 159 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 44 |
| abstract_inverted_index.collect. | 43 |
| abstract_inverted_index.consider | 128, 158 |
| abstract_inverted_index.example, | 9 |
| abstract_inverted_index.greatest | 14 |
| abstract_inverted_index.identify | 106, 124 |
| abstract_inverted_index.planning | 17 |
| abstract_inverted_index.response | 152 |
| abstract_inverted_index.variable | 153 |
| abstract_inverted_index.Explained | 83 |
| abstract_inverted_index.Tanzania. | 103 |
| abstract_inverted_index.Variation | 82 |
| abstract_inverted_index.difficult | 41 |
| abstract_inverted_index.estimates | 24 |
| abstract_inverted_index.household | 31 |
| abstract_inverted_index.including | 164 |
| abstract_inverted_index.introduce | 144 |
| abstract_inverted_index.multiview | 162 |
| abstract_inverted_index.obtaining | 22 |
| abstract_inverted_index.satellite | 62, 88 |
| abstract_inverted_index.structure | 109 |
| abstract_inverted_index.Individual | 81 |
| abstract_inverted_index.approaches | 160 |
| abstract_inverted_index.cell-phone | 65, 91, 114 |
| abstract_inverted_index.important, | 7 |
| abstract_inverted_index.predicted, | 156 |
| abstract_inverted_index.Angle-Based | 78 |
| abstract_inverted_index.Traditional | 19 |
| abstract_inverted_index.call-record | 66 |
| abstract_inverted_index.deprivation | 2, 23, 149 |
| abstract_inverted_index.identifying | 11, 74 |
| abstract_inverted_index.information | 71 |
| abstract_inverted_index.predictors. | 169 |
| abstract_inverted_index.regression, | 163 |
| abstract_inverted_index.traditional | 137 |
| abstract_inverted_index.unavailable | 39 |
| abstract_inverted_index.deprivation. | 75, 125 |
| abstract_inverted_index.incorporating | 134 |
| abstract_inverted_index.interpretable | 107 |
| abstract_inverted_index.interventions. | 18 |
| abstract_inverted_index.nontraditional | 56 |
| abstract_inverted_index.low-dimensional | 108 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.3831888 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |