Global LULC projection dataset from 2020 to 2100 at a 1km resolution

The dataset is on a global scale with a resolution of 1 km grid and encompasses a timespan from 2020 to 2100. These data are projected in the world-Mercator projection coordinate system and are provided in single-band GeoTIFF format, which can be easily utilized by various mainstream GIS and RS platforms such as ArcGIS, QGIS, ENVI, as well as programming languages such as Python and MATLAB. The simulated data files follow a standardized naming convention “sspx_pp_yyyy.tif”, where x represents the simulated SSP scenario (1 to 5), pp represents the simulated RCP scenario; and yyyy represents the simulated year. For example, the data file named “ssp1_26_2030.tif” corresponds to the LULC simulation data for the year 2030 under the SSP1-2.6 scenario. Each GeoTIFF data file includes integer raster attribute values ranging from 1 to 6, which represent the following land use types: cropland, forest, grassland, urban, barren, and water.</p

Visualized analysis of developing trends and hot topics in natural disaster research

<div><p>This study visualized and analyzed the developing trends and hot topics in natural disaster research. 19694 natural disaster-related articles (January 1900 to June 2015) are indexed in the Web of Science database. The first step in this study is using complex networks to visualize and analyze these articles. CiteSpace and Gephi were employed to generate a countries collaboration network and a disciplines collaboration network, and then attached hot topics to countries and disciplines, respectively. The results show that USA, China, and Italy are the three major contributors to natural disaster research. “Prediction model”, “social vulnerability”, and “landslide inventory map” are three hot topics in recent years. They have attracted attention not only from large countries like China but also from small countries like Panama and Turkey. Comparing two hybrid networks provides details of natural disaster research. Scientists from USA and China use image data to research earthquakes. Indonesia and Germany collaboratively study tsunamis in the Indian Ocean. However, Indonesian studies focus on modeling and simulations, while German research focuses on early warning technology. This study also introduces an activity index (AI) and an attractive index (AAI) to generate time evolution trajectories of some major countries from 2000 to 2013 and evaluate their trends and performance. Four patterns of evolution are visible during this 14-year period. China and India show steadily rising contributions and impacts, USA and England show relatively decreasing research efforts and impacts, Japan and Australia show fluctuating activities and stable attraction, and Spain and Germany show fluctuating activities and increasing impacts.</p></div

Relational chart of AI and AAI from 2000 to 2013 for 8 countries/regions, China, India, the U.S., England, Japan, Australia, Spain, and Germany.

<p>The reference line <i>x</i> = <i>y</i> represents the balanced status of AI and AAI of a country/reigon’s natural diaster research.</p

A collaboration network of natural disasters of 160 nodes and 157 links.

<p>Circular nodes represent countries/regions. The size of a circle is in proportion to the number of literatures of the country. The colors of rings of a circle are corresponding to the year. The purple rims of circles represent the high betweenness centralities.</p

Top 10 cited research articles related natural disaster and their citations (with descending order).

<p>Top 10 cited research articles related natural disaster and their citations (with descending order).</p

Geographic distribution of collaborating countries.

<p>Circular nodes represent countries/regions. The size of a circle is in proportion to the number of literatures of the country. The links of nodes represent cooperating relations between countries or regions.</p

A hybrid network of countries (regions) and hot topics of 277 nodes and 251 links.

<p>Circular nodes represent countries/regions. Square nodes represent topics. The color of the ring of a circle are corresponding to the year. The size of a circle is in proportion to the number of literatures of the country. The size of a square is in proportion to the frequency of the topic. The purple rims of nodes represent the high betweenness centralities.</p

A hybrid network of disciplines and hot topics of 194 nodes and 246 links.

<p>Circular nodes represent disciplines. Square nodes represent topics. The color of the ring of a circle is corresponding to the year. The size of a circle is in proportion to the number of literatures of the discipline. The purple rims of circles represent the high betweenness centralities. The size of a square is in proportion to the frequency of the topic.</p