An World Cultural Heritages Monitoring Dataset for Distance between Heritages Sites and Neighbouring Towns (1990-2018)

The past few decades have witnessed unprecedented global urbanisation, with direct or indirect impacts on global cultural heritage sites. Research on the spatial relationship between cultural heritage sites and urban areas has provided a new perspective for understanding the impact processes between them, which have previously been discussed at the regional scale. In the article related, we analyse the spatial relationship between World Cultural Heritage sites and neighbouring towns through systematic observations at the global scale and attempt to model change processes and identify impact mechanisms. We adopt spatial analysis and spatial statistics to analyse the changing characteristics of the spatial relationship between World Cultural Heritage sites and neighbouring towns from 1990 to 2018 and to analyse the impact processes at different spatial and temporal scales by combining indicators such as income levels and urbanisation rates at national scales. This study provides a basis for development plans and policies in urban design, especially those that are sensitive to cultural heritage, and may also provide ideas and references for heritage conservation against the background of urbanisation. The dataset of distance between heritage sites and neighbouring towns (1990-2018), which is the core of this study, is provided here for reference and use by anyone who requires it. The table relates to cultural heritages that have been outside the urban areas between 1990-2018, a total of 523 items. The data items represent the standard ID, name, country, region, location (latitude, longitude) of the heritages sites, distance between heritages sites and neighbouring towns (in km, 1990, 1995, 2000, 2005, 2010, 2015, 2018), and distance variation between the World Heritage Sites and neighbouring towns from 1990-2018 (in km). The data for distance analysis are heritage attribute data and the Global Urban Boundary (GUB) dataset, which is derived from the artificial impervious surface mapping product (Global Artificial Impervious Area (GAIA)). Please refer to the article https://www.sciencedirect.com/science/article/abs/pii/S0034425719305292. Using the ArcGIS near analysis of proximity toolset, we calculated the distance between the input element and the nearest element in another layer or element class within a specified search radius. The method involves concise steps, rapid calculations and accurate results; the search radius is determined from the data, thus avoiding the problem of blindly determining the search range. The calculation steps are as follows:1.  Data inputThe basis for setting the search radius was that experimentally, the farthest distance from a city boundary of all cultural heritage sites worldwide from 1990 to 2018 is 3,918 km, so it is reasonable to choose a search radius of 4,000 km.The basis for choosing the GEODESIC method is because it takes into account the curvature of the spheroid and correctly deals with data near the dateline and poles. The default method PLANAR, on the other hand, calculates only planar distances.2.  Data outputOnce the near tool has been run, the output item fields are automatically added to the World Cultural Heritage table.3.  Data cleaning and statisticsBy observing and sampling the initial distance data and comparing them with the actual situation, we found that the distance calculation results had extreme values and unreasonable situations caused by the boundary extraction error. Therefore, we observed the changes in images of different years. After individual verification, the least squares fitting of some original data was carried out, a small amount of missing data was supplemented, and the extreme value was reasonably corrected.