This study investigates neighborhood scale net migration of young adults in the top 20 urbanized areas (UAs) in the United States between 1980 and 2010. Both descriptive and regression analyses show that Generation Xers and Millennials were more likely to net migrate into central locations and less aversive to high density at their young ages than late boomers were in the 1980s. Consumption amenities are a critical factor that distinguishes the net migration patterns between young and old adult groups and became a more important location factor for young adults in the 2000s (late Gen Xers and older Millennials) than in the 1990s (early Gen Xers). There exists a considerable degree of heterogeneity across UAs and neighborhoods even within the same UAs.

Worldwide, land cover change is monitored by conventional land cover mapping techniques using satellite imagery. Index method ends with assigning positive values to indicate vegetation, wetland and built-up area. However, not all positive values up to a certain threshold specify desired land cover and might indicate other covers erroneously. Therefore, a threshold value must be determined above which land covers are mapped more accurately. In this research, we employed an improved land cover mapping technique to extract vegetation, wetland and built-up area using semiautomatic segmentation approach. We used double-window flexible pace search technique not only for built-up features but also for vegetation and wetland mapping to increase the accuracy. Study is based on Landsat Thematic Mapper images of 1989, 1999 and 2010 with spatial resolution of 30 m. Integration of simple recoding of derived index images prior to threshold identification entails increased accuracy. Accuracy assessment of land cover mapping is done using high-resolution Google Earth satellite image which substitutes expensive aerial photography and time-consuming ground data collection. Error matrix presents 82.46, 96.83 and 90 % user's accuracy of mapping built-up area, vegetation and wetland, respectively. Trend analysis discloses an average loss of vegetation and wetland by 2,664.6 and 5,328.8 acres per year, respectively, in study area from 1989 to 2010. Expectantly, future land cover mapping in similar researches will be greatly assisted with the diligent technique used in this study.Keywords Accuracy assessment Á Double-window flexible pace search Á Error matrix Á Geographic information system Á Land cover mapping Á Remote sensing Á Semiautomatic segmentation approach

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Local government bodies and other concerned agencies in developing countries spend a considerable amount of money on rural road development. However, in the absence of any robust and systematic methodology, road development largely relies on ad-hoc decisions and subjective judgement of public officials. Such a decision-making process often leads to inefficient resource allocation bypassing equity and long-term societal benefits. Although there are some established methodologies for road network planning, complexities exist in applying those methods. First, most of the established methods are not suitable for rural road development, particularly regarding the volume and nature of traffic on them. Second, some methods are highly complex and lack practical applicability. Third, road development planning should not be top-down alone but ensure the participation of local stakeholders. Given these limitations, this study proposes a methodology—Rural Road Planning and Prioritisation Model (RPPM). It consists of two major components (i) developing a core network in participation with local stakeholders and (ii) prioritisation of roads based on Cost-Benefit Analysis (CBA) and Multi-Criteria Analysis (MCA). The proposed method is piloted in one district, and a web-based software is also developed for practical implementation by the Local Government Engineering Department (LGED), Bangladesh. The paper also discusses the results of the pilot study.