The Housing Market and Population Vulnerabilities: Perceptions in a Fordist and a Post‐Fordist Context

Drawing on qualitative interviews and participatory urban‐appraisal tools, this paper analyzes household perceptions on the five elements of Lawrence and Su‐Yeul's market‐led pluralism (M‐LP) framework (2008). It uses post‐Fordist Columbus, Ohio, and Fordist Milwaukee, Wisconsin, as testing grounds for these household perspectives. M‐LP suggests that at the center of contemporary housing market lie five elements: developers/builders who unveil new urban spaces with culturally open communities; lending agencies that offer affordable mortgages to all; real estate brokers/agents who have moved past discriminatory practices; consumers whose preferences emphasize class‐type elements; and communities that impose development agendas. While many felt that the housing market of the mid‐2000s was racially blind, race still played a crucial role in driving home‐buying decisions, especially in Milwaukee. Columbus respondents aligned more closely with M‐LP, putting greater emphasis on class, but they also used phrases and words to blur race and class distinction. Both metropolises, though, illustrated complex intertwining between class and culture.     

GaRiRO: Gradient and residual integrated rank ordering of stations in rainfall monitoring network

This paper presents a new method that integrates gradient and residual values for rank ordering of stations in a monitoring network (GaRiRO). The innovation is derived from the fact that the parameter (dependent variable) gauged through the monitoring network is modelled using independent variables that influence its measured quantity. And the dependent variable exhibit non-stationary spatial gradient with respect to the independent variables, particularly in complex terrain. GaRiRO technique was developed to prioritize the rain gauge stations for optimizing the existing network and selection of the best locations for relocation or installation of gauges. Although initially aimed to assist hydrologists with a ranking scheme for rain gauge stations, it can be applied to any environmental, meteorological or hydro-meteorological monitoring network. The new procedure is based on deriving gradient and residual value at each station by modeling the spatial relationship of dependent-independent variable using geographically weighted regression (GWR) technique. For the prospective stations with no record, the gradient value is estimated using GWR model and the residual value is derived from the residual map generated by applying kriging technique on the residual derived at all gauged locations. The method combines the decision factor with analytical strength of GIS for prioritizing the stations which results in limited number of trials for installation or relocation of gauges to yield optimized network configuration.     

A geographic perspective on intra-urban racial/ethnic diversity, segregation, and clustering in Knoxville, Tennessee: 1990-2000

Racial/ethnic diversity in the United States has increased significantly in recent decades, with minority groups now accounting for almost one-third of the total population. At the same time, growing diversity has spread into rural and non-metropolitan areas. Research suggests that changing diversity in the ‘New South’ has seen growth of non-Black communities. The question, however, is the degree to which increasing diversity equates with increasing intermixing or, alternatively, whether racial/ethnic clusters retain their prominence. This paper examines the geographic manifestations of growing racial/ethnic diversity within intra-urban context, using census-tracts as scale of analysis in the metropolitan statistical area (MSA) of Knoxville in Tennessee. The statistics used for analyzing intra-urban variations include Diversity Score, Theil Entropy Index, and Location Quotient. Tract and Block-group data for White, Black, American Indian, Asian, All Others and Hispanic are used for computing these indices. This paper concludes that diversity has increased during 1990-2000, and has dispersed into suburban counties. However, segregation and clustering for certain minority groups has also increased, in particular African-Americans still remain the most segregated and most clustered community confined to specific geographic locations. This research holds significance as local economic development patterns are very much guided by the geographic variability of human and social capital. Applied research can suggest avenues for growth and can help rebuild local communities. This paper will also contribute to literature focusing on methodological challenges in measuring diversity and its geographic manifestations.     

DEM-based delineation for improving geostatistical interpolation of rainfall in mountainous region of Central Himalayas,India

In mountainous region with heterogeneous topography, the geostatistical modeling of the rainfall using global data set may not confirm to the intrinsic hypothesis of stationarity. This study was focused on improving the precision of the interpolated rainfall maps by spatial stratification in complex terrain. Predictions of the normal annual rainfall data were carried out by ordinary kriging, universal kriging, and co-kriging, using 80-point observations in the Indian Himalayas extending over an area of 53,484 km². A two-step spatial clustering approach is proposed. In the first step, the study area was delineated into two regions namely lowland and upland based on the elevation derived from the digital elevation model. The delineation was based on the natural break classification method. In the next step, the rainfall data was clustered into two groups based on its spatial location in lowland or upland. The terrain ruggedness index (TRI) was incorporated as a co-variable in co-kriging interpolation algorithm. The precision of the kriged and co-kriged maps was assessed by two accuracy measures, root mean square error and Chatfield’s percent better. It was observed that the stratification of rainfall data resulted in 5–20 % of increase in the performance efficiency of interpolation methods. Co-kriging outperformed the kriging models at annual and seasonal scale. The result illustrates that the stratification of the study area improves the stationarity characteristic of the point data, thus enhancing the precision of the interpolated rainfall maps derived using geostatistical methods.     

Socio-economic determinants of women’s livelihood time use in rural Bangladesh

Achieving gender well-being and equality is one of the 17 Sustainable Development Goals of the United Nations. A close examination of female livelihood time allocation can reveal gender inequality in livelihood choices between males and females. Using the feminist political ecology framework, this paper examines how gendered knowledge, roles, and responsibilities influence female livelihood time use in a patriarchal society like Bangladesh. We use a nationally representative household survey data to create Multiple Linear Regression Model to understand the association between economic, cultural, and environmental shocks with the total time allocation toward livelihood activities by women. Our results suggest that use of ‘Purdah’ by Muslim women acts as a negative detrimental factor towards their livelihood time allocation, thus affirming the complex role of culture and gendered economic activities. Women also allocate less time toward livelihood activities during pregnancy and/or breastfeeding. We find that female livelihood time use also depends on their ability to speak in public, their autonomy in livelihood decision processes, and their ownership in business enterprises. This research suggests creating more robust and gender sensitive policies in Bangladesh that can help achieve the United Nation’s goals of Sustainable Development.

     

DCT Based Pansharpening of Satellite Images Using Adaptive Linear Model

Remote sensing offers a wide variety of image data with different characteristics in terms of spatial and spectral resolutions. For optical sensor systems, imaging systems have a trade-off between high spatial and high spectral resolution, and no single system offers both. Hence, in the remote sensing application, an image with ‘greater quality’ often means higher spatial and higher spectral resolution. It is, therefore, necessary and very useful to merge images with higher spectral information and higher spatial information. Pansharpening combines spatial information from the high-resolution panchromatic image and color information from multispectral bands to create a high-resolution color image. Here we propose Discrete Cosine Transform (DCT) based pansharpening algorithm using Adaptive Linear model which preserves spectral information from Multispectral image and retains spatial resolution of Panchromatic image.     

Integrated geospatial,geostatistical, and remote-sensing approach to estimate groundwater level in North-western India

The depletion of groundwater resources in Northwest India has been extensively studied. The top priority to meet the scarcity of water for irrigation, industrial and domestic purposes is supplemented by groundwater. Geostatistical modelling approach is considered to be beneficial tool for the assessment, evaluation, monitoring, and management of groundwater resources. This study is an attempt to analyze the spatio-temporal variability of groundwater level in semi-arid region of Panipat district, Haryana, India using kriging technique to fill the data gaps. Ordinary kriging was found optimal for the interpolation of groundwater levels. The results revealed that there was not much seasonal variation and also the groundwater flow direction remained almost constant in the study area during the assessment period. Spatial variability analysis showed significant variation in groundwater level and further depicted that the study area had undergone more or less decline in groundwater over the period of time. To validate the observations and results geo spatial and remote sensing techniques including normalised difference vegetation index and impervious surface relationships were worked out. It was further co-related with the rainfall data and the canal network existing in Panipat region. The integrated approach substantiated the observed results with the ground reality and helped in better understanding of the causes of declining groundwater trend in central part of Panipat.     

On the modeling of electrical boundary layer (electrode layer) and derivation of atmospheric electrical profiles,eddy diffusion coefficient and scales of electrode layer

Electrode layer or electrical boundary layer is one of the charge generators in the global atmospheric electric circuit. In spite of this we find very few model studies and few measurements of it in the literature. Using a new technique it is shown that in this layer, the space charge density varies exponentially in vertical. A new experimental method based on the surface measurements is discussed to determine all the characteristic scales and an average electrical and meteorological state of an electrode layer. The results obtained are in good agreement with the previous studies. So, it is suggested that an exponential space charge density profile will no longer be an assumption in the case of electrode layer studies. The profiles of atmospheric electric field and electrical conductivity are also derived and a new term named as electrode layer constant is introduced.     

Urban growth and quality of life: inter-district and intra-district analysis of housing in NCT-Delhi, 2001–2011–2020

One out of three people in India is urban. In 2011, there were about 53 urban agglomerations larger than 1 million population as against only 35 in 2001. Much of this urban expansion has been occurring in the country’s largest metropolises including the National Capital Territory of Delhi which has expanded horizontally and vertically both. This has also added to overall decline in its already dilapidated housing stock and quality of life. Delhi, a historical hub for regional, national, and international commerce, and a place for the socio-political elites, has failed to provide basic life amenities to its average citizens. This research critiques the (un)sustainable elements of Delhi’s urbanization and concomitant decline in basic amenities pertaining to quality-of-life by examining the growth and expansion of its urban-built-up areas during 2001–2011–2020 and provides nuanced insights into its ‘livability’ by examining select quality-of-life attributes. The LANDSAT imageries for 2010 and 2020 are used to measure NDB-Index that assesses its built-up area and change, which are later corroborated with Census household data to examine change in its ‘livable’ and ‘dilapidated’ housing structures. Significant sub-regional disparity exists in the availability of good and livable homes, with almost 20–30% of several districts still without drinking water source inside premises. However, significant progress is also noted for basic amenities like lighting, latrine and bathing facilities, and majority of Delhi’s built-up area has expanded along newer developments and transportation corridors. This calls for goal-oriented strategic interventions by policymakers to help achieve the SDG-11 on Sustainable Cities.