Field size distributions and their changes have not been studied over large areas as field size change datasets are not available. This study quantifies agricultural field size changes in a consistent manner using Landsat satellite data that also provide geographic context for the observed decadal scale changes. Growing season cloud-free Landsat 30 m resolution images acquired from 9 to 25 years apart were used to extract field object classifications at seven sites located by examination of a global agricultural yield map, agricultural production statistics, literature review, and analysis of the imagery in the US Landsat archive. High spatial resolution data were used to illustrate issues identifying small fields that are not reliably discernible at 30 m Landsat resolution. The predominant driver of field size change was attributed by literature review. Significant field size changes were driven by different factors, including technological advancements (Argentina and USA), government land use and agricultural policies (Malaysia, Brazil, France), and political changes (Albania and Zimbabwe). While observed local field size changes were complex, the reported results suggest that median field sizes are increasing due to technological advancements and changes to government policy, but may decrease where abrupt political changes affect the agricultural sector and where pastures are converted to arable land uses. In the limited sample considered, median field sizes increased from 45% (France) to 159% (Argentina) and decreased from 47% (Brazil) to 86% (Albania). These changes imply significant impacts on landscape spatial configuration and land use diversity with ecological and biogeochemical consequences.
Malaysia Recovery Movement Control Order (RMCO) aims to bring the business, education, tourism and other industry sectors back into operation. Due to movement constraints that result in local economic patterns, individual mobility patterns are expected to occur. However, this matter needs further investigation from people's spatial behaviour during the RMCO. Therefore, this research proposed a new technique for analysing people's spatial behaviour patterns via geo-tagged data. The data from social media users are gathered using data mining techniques. Geographical Information System (GIS) is used to show the geolocation of social media users and analyse their spatial behaviour. The finding of this analysis shows higher people's movement recorded when the RMCO was enforced; a distinctive pattern where spatial trajectory length is high but spatial area coverage is low. It is noticed that the focal points are concentrated in urban areas and tourism attractions.