Correspondence Clustering: An Approach to Cluster Multiple Related Spatial Datasets

The degradation of pastures is one of Brazil's biggest problems today and directly affects the sustainability of livestock. The animal production in a degraded pasture can be six times smaller than a grazing or recovered in good maintenance state. So we can consider that productivity could be increased in pasture areas, and analyze how productivity is limited by biophysical factors (climate, for example) versus management. Using spatial datasets, we compare yield patterns for the pasturelands within regions of similar climate. We use this comparison to evaluate the potential yield obtainable for pasturelands in different climates around the Brazil using the limits of Brazilian biomes. We then compare the actual yields currently being achieved with their ‘potential yield’ to estimate the ‘yield gap’, present spatial datasets of both the potential yields and yield gap patterns for pasturelands around the year 1995 and 2006. This study is intended to be an important new resource for scientists and policymakers alike, helping to more accurately understand spatial variation of yield and agricultural intensification potential, as well as employing these data to better utilize existing infrastructure and optimize the distribution of development and aid capital.

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A class of multi-resolution approximations for large spatial datasets

Statistica Sinica ◽

10.5705/ss.202018.0285 ◽

2020 ◽

Cited By ~ 1

Author(s):

Matthias Katzfuss ◽

Wenlong Gong

Keyword(s):

Spatial Datasets

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Mixture of Regression Models for Large Spatial Datasets

Technometrics ◽

10.1080/00401706.2019.1569558 ◽

2019 ◽

Vol 61 (4) ◽

pp. 507-523 ◽

Cited By ~ 1

Author(s):

Karen Kazor ◽

Amanda S. Hering

Keyword(s):

Regression Models ◽

Spatial Datasets

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Global restoration opportunities in tropical rainforest landscapes

Science Advances ◽

10.1126/sciadv.aav3223 ◽

2019 ◽

Vol 5 (7) ◽

pp. eaav3223 ◽

Cited By ~ 80

Author(s):

Pedro H. S. Brancalion ◽

Aidin Niamir ◽

Eben Broadbent ◽

Renato Crouzeilles ◽

Felipe S. M. Barros ◽

...

Keyword(s):

Tropical Rainforest ◽

Anthropogenic Impacts ◽

Cost Effective ◽

Well Being ◽

Area Network ◽

Spatial Datasets ◽

Potential Benefits ◽

The Tropics

Over 140 Mha of restoration commitments have been pledged across the global tropics, yet guidance is needed to identify those landscapes where implementation is likely to provide the greatest potential benefits and cost-effective outcomes. By overlaying seven recent, peer-reviewed spatial datasets as proxies for socioenvironmental benefits and feasibility of restoration, we identified restoration opportunities (areas with higher potential return of benefits and feasibility) in lowland tropical rainforest landscapes. We found restoration opportunities throughout the tropics. Areas scoring in the top 10% (i.e., restoration hotspots) are located largely within conservation hotspots (88%) and in countries committed to the Bonn Challenge (73%), a global effort to restore 350 Mha by 2030. However, restoration hotspots represented only a small portion (19.1%) of the Key Biodiversity Area network. Concentrating restoration investments in landscapes with high benefits and feasibility would maximize the potential to mitigate anthropogenic impacts and improve human well-being.

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Extracting the Boundaries of Clusters: A Post-Clustering Tool for Spatial Datasets

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420500081 ◽

2019 ◽

Vol 34 (04) ◽

pp. 2050008

Author(s):

Tülin İnkaya ◽

Sinan Kayalıgil ◽

Nur Evin Özdemirel

Keyword(s):

Delaunay Triangulation ◽

Numerical Experiments ◽

Boundary Extraction ◽

3 Dimensional ◽

Clustering Problem ◽

Spatial Datasets

Boundary extraction is a fundamental post-clustering problem. It facilitates interpretability and usability of clustering results. Also, it provides visualization and dataset reduction. However, it has not attracted much attention compared to the clustering problem itself. In this work, we address the boundary extraction of clusters in 2- and 3-dimensional spatial datasets. We propose two algorithms based on Delaunay Triangulation (DT). Numerical experiments show that the proposed algorithms generate the cluster boundaries effectively. Also, they yield significant amounts of dataset reduction.

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