Characterizing Large Scale Land Acquisitions Through Network Analysis

Interdonato, Roberto; Bourgoin, Jérémy; Grislain, Quentin; Zignani, Matteo; Gaito, Sabrina; Giger, Markus

doi:10.1007/978-3-030-36683-4_13

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…To this purpose, we will model open access data about LSLAs extracted from the Land Matrix Initiative database ( https://landmatrix.org ) into a network graph, and adapt an eigenvector based centrality method originally conceived for online social networks, namely LurkerRank [ 17 – 19 ], to identify and rank anomalous profiles in the land trade market. While the effectiveness of complex network analysis techniques on the analysis of the land trade market has already been confirmed by our previous works on such topic [ 15 , 16 ], the scenario we study in this work requires a methodology with peculiar characteristics. In particular, we need a method able to capture at the same time the centrality of a country in the land trade market, and the entity of the acquired/sold land asymmetry representing the anomaly (i.e., overconsumption principle).…”

Section: Introductionmentioning

confidence: 81%

“…However, recent research work based on complex network analysis [ 15 , 16 ] confirmed that other important dynamics exist in the global land trade market, such as a South-South one (e.g., Latin American countries investing in Africa), and the one involving emerging economies, such as the so-called BRICS countries (Brazil, Russia, India, China and South Africa). More specifically, in [ 16 ] we defined the LSLA-score , a topology-based measure proportional to the ratio between sold and acquired land for each country, which enables the ranking of countries based on their investing/target role in the land trade network.…”

Section: Introductionmentioning

confidence: 99%

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The new abnormal: Identifying and ranking anomalies in the land trade market

Interdonato¹,

Bourgoin²,

Grislain³

et al. 2022

PLoS ONE

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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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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

The new abnormal: Identifying and ranking anomalies in the land trade market

Interdonato¹,

Bourgoin²,

Grislain³

et al. 2022

PLoS ONE

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show abstract

“…The aim of their study is more to identify general trends that characterize different temporal phases of the land trade market, while in this work we want to focus on complex relations among countries, by also considering heterogeneity of the deals, namely the intention of investment and the implementation status. In [44], we published some preliminary results regarding the use of network analysis techniques for the analysis and characterization of LSLAs phenomena. The aim of this work is to push forward this promising approach based on complex network analysis, by providing a wider quantitative and qualitative analysis structured in the following main contributions:…”

Section: Introductionmentioning

confidence: 99%

The parable of arable land: Characterizing large scale land acquisitions through network analysis

et al. 2020

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Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasing oil prices and rise of green capitalism that in turn impacts the demand in biofuel. A visible indicator of these phenomena is the increase, in recent years, of Large Scale Land Acquisitions (LSLAs) by private companies or states. Such land investments often lead to conflicts with local population and have raised issues regarding people's rights, the role of different production models and land governance. The aim of this work is to show how publicly available data about LSLAs can be modeled into complex network structures, thus showing how the application of advanced network analysis techniques can be used to better understand land trade dynamics. We use data collected by the Land Matrix Initiative on LSLAs to model three land trade networks: a multi-sector network, a network centered on the mining sector and a network centered on the agriculture one. Then we provide an extended analysis of such networks which includes: (i) a structural analysis, (ii) the definition of a score, namely LSLAscore, which allows to rank the countries based on their investing/target role in the land trade network, (iii) an analysis of the land trade context which takes into account the LSLAscore ranking and the correlation between network features and several country development indicators, (iv) an analysis centered on the discover and analysis of network motifs (i.e., recurring patterns in the land trade network), which provides insights into complex and diverse relations between countries. Our analyses showed how the land trade market is massively characterized by a Global North-Global South dynamic, even if the investing power of emerging economies also has a major impact in creating relations between different sub-regions of the world. Moreover, the analyses on the mining and agriculture sectors highlighted how the role of several countries in the trade network may drastically change depending of the investment sector, showing diverse hierarchies between investor, intermediate and target countries.

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Characterizing Large Scale Land Acquisitions Through Network Analysis

Cited by 2 publications

References 8 publications

The new abnormal: Identifying and ranking anomalies in the land trade market

The new abnormal: Identifying and ranking anomalies in the land trade market

The parable of arable land: Characterizing large scale land acquisitions through network analysis

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