The new abnormal: Identifying and ranking anomalies in the land trade market Article Swipe
Roberto Interdonato
,
Jérémy Bourgoin
,
Quentin Grislain
,
Andrea Tagarelli
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0277608
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0277608
Large-scale national and transnational commercial land transactions, or Large-Scale Land Acquisitions (LSLAs), have been gaining a lot of academic attention since the late 2000s and since the reported rush for land, resulting in turn from an increase in demand for arable land. If many data exist to characterize land deals, the analysis of investment networks remain limited and predominantly portrays power asymmetries between countries from the Global North investing in the Global South. The aim of this work is to perform a deeper investigation on the land trade market, specifically focusing on cases that do not follow such narratives. For instance, almost 25% of the countries included in the transnational land trade network do not follow a strict investor/target dichotomy, thus being characterized by a double role, i.e., they both acquire and cede land in the transnational context. In order to globally acknowledge for what was currently considered as abnormal cases, we model open access data about LSLAs extracted from the Land Matrix Initiative (LMI) open-access database into a network graph, and adapt an eigenvector based centrality method originally conceived for online social networks, namely LurkerRank , to identify and rank anomalous profiles in the land trade market. We take into account three different network snapshots: a multi-sector network (including all the transnational deals in the LMI database), and three networks referring to specific investment sectors ( agriculture , mines and biofuels ). Experimental results show that emerging economies (e.g., China and Malaysia) play a central role in the land trade market, by creating alternative dynamics that escape the classic North/South one. Our analyses also show how African countries that are often seen as targets of land trade transactions in a specific sector, may often acquire foreign land in the context of investments in the same sector (i.e., Zimbabwe for biofuels and the Democratic Republic of Congo for the mining sector).
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0277608
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0277608&type=printable
- OA Status
- gold
- Cited By
- 1
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4311069422
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4311069422Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0277608Digital Object Identifier
- Title
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The new abnormal: Identifying and ranking anomalies in the land trade marketWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-01Full publication date if available
- Authors
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Roberto Interdonato, Jérémy Bourgoin, Quentin Grislain, Andrea TagarelliList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0277608Publisher landing page
- PDF URL
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0277608&type=printableDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0277608&type=printableDirect OA link when available
- Concepts
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Context (archaeology), Land grabbing, Centrality, Arable land, Business, Investment (military), Social network analysis, International trade, Economics, Geography, Agriculture, Computer science, Political science, Politics, World Wide Web, Social media, Archaeology, Law, Combinatorics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.double | 125 |
| abstract_inverted_index.escape | 257 |
| abstract_inverted_index.follow | 96, 115 |
| abstract_inverted_index.graph, | 170 |
| abstract_inverted_index.method | 177 |
| abstract_inverted_index.mining | 309 |
| abstract_inverted_index.namely | 184 |
| abstract_inverted_index.online | 181 |
| abstract_inverted_index.remain | 55 |
| abstract_inverted_index.sector | 296 |
| abstract_inverted_index.social | 182 |
| abstract_inverted_index.strict | 117 |
| abstract_inverted_index.African | 267 |
| abstract_inverted_index.account | 201 |
| abstract_inverted_index.acquire | 130, 285 |
| abstract_inverted_index.between | 62 |
| abstract_inverted_index.central | 245 |
| abstract_inverted_index.classic | 259 |
| abstract_inverted_index.context | 290 |
| abstract_inverted_index.foreign | 286 |
| abstract_inverted_index.gaining | 14 |
| abstract_inverted_index.limited | 56 |
| abstract_inverted_index.market, | 88, 251 |
| abstract_inverted_index.market. | 197 |
| abstract_inverted_index.network | 112, 169, 204, 208 |
| abstract_inverted_index.perform | 80 |
| abstract_inverted_index.results | 234 |
| abstract_inverted_index.sector, | 282 |
| abstract_inverted_index.sectors | 225 |
| abstract_inverted_index.targets | 274 |
| abstract_inverted_index.(LSLAs), | 11 |
| abstract_inverted_index.Republic | 304 |
| abstract_inverted_index.Zimbabwe | 298 |
| abstract_inverted_index.abnormal | 149 |
| abstract_inverted_index.academic | 18 |
| abstract_inverted_index.analyses | 263 |
| abstract_inverted_index.analysis | 51 |
| abstract_inverted_index.biofuels | 231, 300 |
| abstract_inverted_index.context. | 137 |
| abstract_inverted_index.creating | 253 |
| abstract_inverted_index.database | 166 |
| abstract_inverted_index.dynamics | 255 |
| abstract_inverted_index.emerging | 237 |
| abstract_inverted_index.focusing | 90 |
| abstract_inverted_index.globally | 141 |
| abstract_inverted_index.identify | 188 |
| abstract_inverted_index.included | 106 |
| abstract_inverted_index.increase | 36 |
| abstract_inverted_index.national | 1 |
| abstract_inverted_index.networks | 54, 220 |
| abstract_inverted_index.portrays | 59 |
| abstract_inverted_index.profiles | 192 |
| abstract_inverted_index.reported | 27 |
| abstract_inverted_index.sector). | 310 |
| abstract_inverted_index.specific | 223, 281 |
| abstract_inverted_index.Malaysia) | 242 |
| abstract_inverted_index.anomalous | 191 |
| abstract_inverted_index.attention | 19 |
| abstract_inverted_index.conceived | 179 |
| abstract_inverted_index.countries | 63, 105, 268 |
| abstract_inverted_index.currently | 146 |
| abstract_inverted_index.different | 203 |
| abstract_inverted_index.economies | 238 |
| abstract_inverted_index.extracted | 158 |
| abstract_inverted_index.instance, | 100 |
| abstract_inverted_index.investing | 68 |
| abstract_inverted_index.networks, | 183 |
| abstract_inverted_index.referring | 221 |
| abstract_inverted_index.resulting | 31 |
| abstract_inverted_index.(including | 209 |
| abstract_inverted_index.Democratic | 303 |
| abstract_inverted_index.Initiative | 163 |
| abstract_inverted_index.LurkerRank | 185 |
| abstract_inverted_index.centrality | 176 |
| abstract_inverted_index.commercial | 4 |
| abstract_inverted_index.considered | 147 |
| abstract_inverted_index.database), | 217 |
| abstract_inverted_index.dichotomy, | 119 |
| abstract_inverted_index.investment | 53, 224 |
| abstract_inverted_index.originally | 178 |
| abstract_inverted_index.snapshots: | 205 |
| abstract_inverted_index.Large-Scale | 8 |
| abstract_inverted_index.Large-scale | 0 |
| abstract_inverted_index.North/South | 260 |
| abstract_inverted_index.acknowledge | 142 |
| abstract_inverted_index.agriculture | 227 |
| abstract_inverted_index.alternative | 254 |
| abstract_inverted_index.asymmetries | 61 |
| abstract_inverted_index.eigenvector | 174 |
| abstract_inverted_index.investments | 292 |
| abstract_inverted_index.narratives. | 98 |
| abstract_inverted_index.open-access | 165 |
| abstract_inverted_index.Acquisitions | 10 |
| abstract_inverted_index.Experimental | 233 |
| abstract_inverted_index.characterize | 47 |
| abstract_inverted_index.multi-sector | 207 |
| abstract_inverted_index.specifically | 89 |
| abstract_inverted_index.transactions | 278 |
| abstract_inverted_index.characterized | 122 |
| abstract_inverted_index.investigation | 83 |
| abstract_inverted_index.predominantly | 58 |
| abstract_inverted_index.transactions, | 6 |
| abstract_inverted_index.transnational | 3, 109, 136, 212 |
| abstract_inverted_index.investor/target | 118 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.67730623 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |