Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar

Abstract

Understanding human mobility patterns provides knowledge about impacts of a socio-economic transformation in a rapidly urbanizing environment. This study assesses a long-term mobility data which uses a face-to-face questionnaire and GPS logger-based method of data collection for extracting socio-economic impacts from the rapid transformation. Conversion of mobility related information such as travel distance, direction, and time from the questionnaire survey into spatiotemporal information was carried out by developing an algorithm. To illustrate the proposed approach, a case study in Dawei Special Economic Zone, Myanmar was conducted. The results show that the questionnaire-based mobility data can be associated with GPS-based mobility data and diverse mobility patterns are found for different social groups in the stage of urban formation. The results enabled an understanding of the human dynamics in interactions, which can be used for monitoring rural sustainability and its challenges in the future with the background of the accelerated project development in the area.

Time-Series Assessment of Camp-Type Artisanal and Small-Scale Gold Mining Sectors with Large Influxes of Miners Using LANDSAT Imagery

“…Since the transformation by ASGM in remote rural areas is associated with influxes of miners who live at mining camps, as described in the Introduction, the spatial distribution of built-up areas was extracted as a significant indicator of the transformation of the ASGM camps resulting from large influxes of miners. Built-up areas can be defined by their physical aspects, such as predominantly human-constructed elements [ 42 ], as in this study. A number of spectral indices, including the Urban Index (UI) [ 43 ], Normalized Difference Built-up Index (NDBI) [ 44 ], Index-based Built-up Index [ 45 ], Built-up Area Extraction Method [ 46 ], Enhanced Built-up and Bareness Index [ 47 ], Band Ratio for Built-up Area [ 48 ], Built-up Index [ 49 ], Normalized Difference Vegetation Index (NDVI) [ 34 ], and Automated Built-up Extraction Index [ 50 ], have been developed to extract built-up areas from satellite imagery.…”

“…A number of spectral indices, including the Urban Index (UI) [ 43 ], Normalized Difference Built-up Index (NDBI) [ 44 ], Index-based Built-up Index [ 45 ], Built-up Area Extraction Method [ 46 ], Enhanced Built-up and Bareness Index [ 47 ], Band Ratio for Built-up Area [ 48 ], Built-up Index [ 49 ], Normalized Difference Vegetation Index (NDVI) [ 34 ], and Automated Built-up Extraction Index [ 50 ], have been developed to extract built-up areas from satellite imagery. Furthermore, human visual interpretation was also used [ 42 ]. Previous studies found that NDBI [ 51 , 52 ] and UI [ 53 , 54 ] are most sensitive in retrieving built-up areas, although these have mainly been used in urban studies.…”

Detection of Artisanal and Small-Scale Gold Mining Activities and Their Transformation Using Earth Observation, Nighttime Light, and Precipitation Data

“…In this study, the built-up areas were defined based on their physical aspects, such as a built-up environment consisting mostly of human-constructed elements [33]. A number of spectral indices, together with human visual interpretation [33][34][35][36][37][38][39][40][41], were employed to detect built-up areas using remote-sensing technology.…”

“…In this study, the built-up areas were defined based on their physical aspects, such as a built-up environment consisting mostly of human-constructed elements [33]. A number of spectral indices, together with human visual interpretation [33][34][35][36][37][38][39][40][41], were employed to detect built-up areas using remote-sensing technology. Previous studies found that the Normalized Difference Built-up Index (NDBI) [42,43] and the Urban Index (UI) [44,45] had high sensitivities for retrieving built-up areas; however, these have been employed mainly in urban studies.…”