Members’ Publication

This book critically examines different forms of urban-rural links for sustainable development in different countries. As intertwined processes of globalization, digitalization, environmental challenges and the search for sustainable development continue, rural and urban areas around the world become increasingly interconnected and interdependent. This book contributes to understanding the role of this growing interconnectedness from an economic geographical perspective. It does so by theoretically and empirically addressing the various existing linkages, such as food networks, value chains, and regional governance at local, regional, national and international levels. In doing so, contributions extend and contrast existing approaches dealing with urban and rural areas separately by considering the interplay between these two as well as their consequences for sustainability transition pathways. This edited volume adds to the academic and policy debate by bringing together a variety of concepts and themes in order to shift the research and policy agenda away from simple dichotomy to different notions of rural-urban linkages. Offering multidisciplinary insights into rural-urban linkages, the book will be of interest to decision-makers, practitioners and researchers in the fields of economic geography, regional planning, food studies and economics.

Perceptions of the walking environment can encourage or discourage walking for transport. However, the influence of the built environment (BE) on pedestrians’ perceptions of the walking environment has not been fully understood. To address this gap, the present research investigates how BE characteristics of a suburban walking environment are associated with the perceptions of attractiveness (i.e., pleasantness, friendliness), safety, and security. Using a cross-sectional design, 995 participants reported their perceptions about the attractiveness, safety, and security of different suburban BE scenarios in Brisbane (Australia). Univariate and bivariate random effect ordered probit models were estimated to identify the associations between BE characteristics and perceived attractiveness, safety, and security whilst controlling for psychosocial factors. Results indicated that perceived attractiveness (i.e., pleasantness and friendliness) was higher for recreational areas than residential land use. Women perceived the walking environment as more pleasant with trees. The perception of safety was higher for recreational and vacant land compared to residential areas. Young pedestrians perceived that the likelihood of being assaulted/robbed/harassed at night was lower if they walked through an area with commercial and mixed land use. Women pedestrians perceived that the corresponding risk at night was higher in recreational areas. Overall, the findings suggest that urban design strategies such as increasing land use diversity and providing adequate trees enhance perceived environmental attractiveness, safety, and security, ultimately resulting in more walking for transport.

Changes in land use/land cover (LULC) and land surface temperatures (LST) contribute significantly to the formation and intensity of urban heat islands (UHI) effects. The urban thermal field variance index (UTFVI) can effectively describe any city's UHI (thermal characteristics) effect. This study aims to assess and predict the seasonal (summer and winter) UTFVI scenario to evaluate the thermal characteristics of Sylhet city, Bangladesh. Landsat 4–5 TM and 8 OLI images from 1995 to 2020 were used to assess the previous status of LULC and UTFVI and predict the future changes for 2025 and 2030 using cellular automata and artificial neural network machine learning algorithms. Prediction results indicate a substantial increase in urban built-up areas by 42% and 44% in 2025 and 2035, followed by reductions in green cover (21% and 22%), bare land (20% and 21%) and water bodies (1%). The rapid expansion of built-up areas will lead to 13 km2 and 14 km2 stronger UTFVI zones in the predicted years. The study provides effective strategies for mitigating the UTFVI effects by avoiding dense infrastructural development, increasing plantation and water bodies, rooftop gardening and using white colour roofs in construction. The findings of this study will allow the urban planners, policymakers and local government to ensure an eco-friendly, inclusive and sustainable urban development through functional modification and replacement of the LULC distribution depending on the present and future circumstances.

Flood is one of the most devastating climatic disasters around the world. The physical and infrastructural damage of floods is uncontrollable and challenging to recover. Though the mismanagement of the water system is one cause of flood, some countries face a seasonal natural flood, which is impossible to avoid. The history of flood affection in these countries are long, and people don't have any other choice but to adapt to this circumstance every year. The continuous flood event has several climatic impacts that are not broadly documented and remain in the shadows of severe infrastructural damage. This research aims to identify the effect of monsoon floods on vegetation and land surface temperature (LST). The study area is the northeast part of Bangladesh, a highly flood-prone area. The research incorporates Google Earth Engine (GEE) to manage the satellite image data related to this research which are Sentinel-1 SAR imagery and Landsat-8 imagery. Six years of data from 2015 to 2020 have been taken to continuously monitor the flooded area, vegetation, and LST dynamics. Primary results indicate a yearly increase and decrease of the flooded area with 57.3% highest increase rate in 2019. A continuous increase of Enhanced Vegetation Index (EVI) value and decrease of LST has a changing pattern similarity with flooded area fluctuation over the year. Also, the flooded areas have around 50% less mean EVI value than the non-flooded areas, eventually rising average LST in flooded areas. 10,024 grids of 1 km × 1 km have been used to extensively analyze the relationship of flood and EVI through correlation and linear regression. The final result reveals a clear negative correlation value (less than 0.56 for all the years) of EVI with flooded areas, having the highest R-squared value of 0.4325 in 2017.

Flood is one of the most devastating climatic disasters around the world. The physical and infrastructural damage of floods is uncontrollable and challenging to recover. Though the mismanagement of the water system is one cause of flood, some countries face a seasonal natural flood, which is impossible to avoid. The history of flood affection in these countries are long, and people don't have any other choice but to adapt to this circumstance every year. The continuous flood event has several climatic impacts that are not broadly documented and remain in the shadows of severe infrastructural damage. This research aims to identify the effect of monsoon floods on vegetation and land surface temperature (LST). The study area is the northeast part of Bangladesh, a highly flood-prone area. The research incorporates Google Earth Engine (GEE) to manage the satellite image data related to this research which are Sentinel-1 SAR imagery and Landsat-8 imagery. Six years of data from 2015 to 2020 have been taken to continuously monitor the flooded area, vegetation, and LST dynamics. Primary results indicate a yearly increase and decrease of the flooded area with 57.3% highest increase rate in 2019. A continuous increase of Enhanced Vegetation Index (EVI) value and decrease of LST has a changing pattern similarity with flooded area fluctuation over the year. Also, the flooded areas have around 50% less mean EVI value than the non-flooded areas, eventually rising average LST in flooded areas. 10,024 grids of 1 km × 1 km have been used to extensively analyze the relationship of flood and EVI through correlation and linear regression. The final result reveals a clear negative correlation value (less than 0.56 for all the years) of EVI with flooded areas, having the highest R-squared value of 0.4325 in 2017.