An Assessment of Urban Expansion Impacts on Land Transformation of Rajpur-Sonarpur Municipality

Urban expansion, population pressure, and increasing industrialization are influenced the world's natural environment, increased climate change, and global warming. Land use and land cover (LULC) changes are occurring due to anthropogenic activities and the results are land transformation, thermal variation, heat stress, and massive vegetation loss, which is increased oxygen deficiency and air pollution. Remote sensing-based Landsat TM and OLI/TIRS data were used to identify the land transformation and the effect of urbanization in Rajpur-Sonarpur Municipality in the years 2000 and 2020. The Built-up land was increased due to anthropogenic activities and around 12.16% area has been increased. Similarly, the agricultural land has been increased by 2.10 km2 area and vegetation area has been decreased around 6.52 km2 in the study area. The vegetation degradation areas were located in the South, South-west, and central parts of this study area. Correspondingly, North, North-west and central parts have a noticeable amount of built-up area developed. During twenty years, 6.97°C temperature was increased due to urban expansion, population pressure, transportation accessibility, and other urban amenities. The overall accuracies of classification maps were 96.25% and 91.64 % and the kappa coefficients were 0.95 and 0.89 respectively.

Reconnecting Religion and Community in a Small City: How Urban Amenities Afford Religious Amenities

Recently, sociologists of religion have argued that rather than treating geographical location as a mere backdrop against which religion happens, scholars ought to theorize how place characteristics influence, and are shaped by, religion. In particular, they focus on urbanicity, a key variable in the secularization debate. Drawing on interviews with 50 Catholic and non-Catholic residents of a small city just outside of Washington, D.C. along with participant observation data, I argue that one way to examine how urbanicity—and space and place more generally—matters for religion is to identify its affordances, or features of an environment that allow for certain lines of action. Specifically, I show how urban amenities can afford the creation of religious amenities that support religious practice. I also demonstrate how the concepts of affordances and amenities can be used to theorize place characteristics, and their relationship with religion, more systematically.

Analysis on Household Income to the Indigenous Aetas of Pampanga Philippines

Indigenous people (IPs) are the descendants of the inhabitants of a country or region. This study examines whether an indigenous group, specifically an Aeta community in Pampanga, undergoes Urbanization or not. Migration has been a part of the Aeta's history since the eruption of Mt. Pinatubo, which forced them to move out from their original lands. Despite the Urbanization of the community, their culture and way of living remained the same and didn't change. The Aetas pass it on to their next generation, and due to this, they suffer from discrimination because of their way of living. To gather the needed data, the researchers reviewed various literature and studies to have an idea of how to construct the survey question based on LSMS and DHS. The study was conducted in Barangay Sapang Uwak, Sitio Pidpid, Porac, Pampanga, a home of an Aeta community. By looking at the field site, it shows that development and accessibility of urban amenities are difficult for them since it was evident to the researchers that education and some necessities are insufficient.

Elevating the Value of Urban Location: A Consumer Preference-Based Approach to Valuing Local Amenity Provision

Estimating the non-market monetary values of urban amenities has become commonplace in urban planning research, particularly following Rosen’s seminal article on hedonic theory in 1974. As a revealed preference method, the hedonic approach decouples the market price of a house into price components that are attributable to housing characteristics. Despite the potential contribution of this theory in a planning context, three main limitations exist in the conventional applications: (1) variable measurement issues, (2) model misspecification, and (3) the problematic common use of global regression. These flaws problematically skew our understanding of the urban structure and spatial distribution of amenities, leading to misinformed policy interventions and poor amenity planning decisions. In this article, we propose a coherent conceptual framework that addresses measurement, specification, and scale challenges to generate consistent economic estimates of local amenities. Finally, we argue that, by paying greater attention to the spatial equity of amenity values, governments can provide greater equality of opportunities in cities.

Localizando trabajadores del conocimiento en grandes regiones urbanas: un análisis integrado de las trayectorias personales y los factores de atracción y retención en Madrid

La atracción y retención de los trabajadores del conocimiento son considerados vectores fundamentales para el desarrollo urbano. Esto explica el interés por comprender las fuerzas que guían la localización de estos trabajadores y las bases del atractivo de las principales ciudades. Esta investigación parte del esquema interpretativo de estudios previos a nivel europeo y busca analizar las trayectorias y los motivos de los trabajadores del conocimiento para localizarse en la región de Madrid, a partir de la realización de una encuesta online ad hoc. Los resultados confirman la importancia para los recién llegados de los factores hard, relacionados con las oportunidades de empleo, por encima de factores soft, relacionados con la disponibilidad de urban amenities y los estilos de vida, que parecen influir en la retención de algunos individuos, especialmente de aquellos que más tiempo llevan en la región. A ello contribuyen también factores network, como las redes familiares y profesionales. Todo ello tiene implicaciones para las políticas urbanas.

Subjectively Measured Streetscape Qualities for Shanghai with Large-Scale Application of Computer Vision and Machine Learning

Recently, many new studies emerged to apply computer vision (CV) to street view imagery (SVI) dataset to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities. However, human perceptions (e.g., imageability) have a subtle relationship to visual elements which cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain more human behaviors. However, the effectiveness of integrating subjective measures with SVI dataset has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected experts’ rating on sample SVIs regarding the four qualities which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting the scores. We found a strong correlation between predicted complexity score and the density of urban amenities and services Point of Interests (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five renowned urban cores worldwide. Rather than predicting perceptual scores directly from generic image features using convolution neural network, our approach follows what urban design theory suggested and confirms various streetscape features affecting multi-dimensional human perceptions. Therefore, its result provides more interpretable and actionable implications for policymakers and city planners.

The impact of COVID-19 on trips to urban amenities: Examining travel behavior changes in Somerville, MA

While there has been much speculation on how the pandemic has affected work location patterns and home location choices, there is sparse evidence regarding the impacts that COVID-19 has had on amenity visits in American cities, which typically constitute over half of all urban trips. Using aggregate app-based GPS positioning data from smartphone users, this study traces the changes in amenity visits in Somerville, MA from January 2019 to December 2020, describing how visits to particular types of amenities have changed as a result of business closures during the public health emergency. Has the pandemic fundamentally shifted amenity-oriented travel behavior or is consumer behavior returning to pre-pandemic trends? To address this question, we calibrate discrete choice models that are suited to Census block-group level analysis for each of the 24 months in a two-year period, and use them to analyze how visitors’ behavioral responses to various attributes of amenity clusters have shifted during different phases of the pandemic. Our findings suggest that in the first few months of the pandemic, amenity-visiting preferences significantly diverged from expected patterns. Even though overall trip volumes remained far below normal levels throughout the remainder of the year, preferences towards specific cluster attributes mostly returned to expected levels by September 2020. We also construct two scenarios to explore the implications of another shutdown and a full reopening, based on November 2020 consumer behavior. While government restrictions have played an important role in reducing visits to amenity clusters, our results imply that cautionary consumer behavior has played an important role as well, suggesting a likely long and slow path to economic recovery. By drawing on mobile phone location data and behavioral modeling, this paper offers timely insights to help decision-makers understand how this unprecedented health emergency is affecting amenity-related trips and where the greatest needs for intervention and support may exist.

The Interregional Migration of Human Capital: The Case of “First-Class” University Graduates in China

Human capital has been acknowledged as a key driver for innovation, thereby promoting regional economic development in the knowledge era. University graduates from China’s “first-class” universities—the top 42 universities, included in the “double first-class” initiative, are considered highly educated human capital. Their migration patterns will exert profound impacts on regional development in China, however, little is known about the migration of these elite university graduates and its underlying driving forces. Using data from the 2018 Graduate Employment Reports, this study reveals that the uneven distribution of “first-class” universities and regional differentials largely shaped the migration of graduates from the university to work. Graduates were found aggregating in eastern first-tier cities, even though appealing talent-orientated policies aimed at attracting human capital had been launched in recent years by second-tier cities. Employing negative binomial models, this study investigates how the characteristics of the city of university and destinations affect the intensity of flows of graduates between them. The results showed that both jobs and urban amenities in the university city and destination city exert impacts on the inflow volume of graduates; whereas talent attraction policies introduced by many second-tier cities are found not to exert positive effects on attracting “first-class” university graduates presently. The trend of human capital migration worth a follow-up investigation, particularly given ongoing policy dynamics, and would shed light on the regional development disparities in China.

Do Creative Industries Generate Multiplier Effects? Evidence from UK Cities, 1997-2018

The creative industries have received much attention from economic geographers and others, both for their propensity to co-locate in urban settings and their potential to drive urban economic development. However, evidence on the latter is surprisingly sparse. In this paper we explore the long-term, causal impacts of the creative industries on surrounding urban economies. Adapting Moretti’s local multipliers framework, we build a new 20-year panel of UK cities, using fixed effects and a historic instrument to identify effects on non-creative firms and employment. We find that each creative job generates at least 1.9 non-tradable jobs between 1998 and 2018: this is associated with creative business services employees’ local spending, rather than visitors to urban amenities such as galleries and museums. We do not find the same effects for workplaces, and find no causal evidence for spillovers from creative activity to other tradable sectors, findings consistent with descriptive evidence on the increasing concentration of creative industries in a small number of cities. Given the small numbers of creative jobs in most cities, however, the overall effect size of the creative multiplier is small, and shapes only a small part of non-tradable urban employment change. Overall, our results suggest creative economy-led policies for cities can have positive – albeit partial – local economic impacts.

Subjectively Measured Streetscape Perceptions to Inform Urban Design Strategies for Shanghai

Recently, many new studies applying computer vision (CV) to street view imagery (SVI) datasets to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities have emerged. However, human perception (e.g., imageability) have a subtle relationship to visual elements that cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain human behaviors more. However, the effectiveness of integrating subjective measures with SVI datasets has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected ratings from experts on sample SVIs regarding these four qualities, which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting scores. We found a strong correlation between the predicted complexity score and the density of urban amenities and services points of interest (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five urban cores that are renowned worldwide. Rather than predicting perceptual scores directly from generic image features using a convolution neural network, our approach follows what urban design theory has suggested and confirmed as various streetscape features affecting multi-dimensional human perceptions. Therefore, the results provide more interpretable and actionable implications for policymakers and city planners.