Urban Change, a Loss of Centrality, and New Destinies for Downtowns

2009 ◽  
pp. 1-30
Keyword(s):  
Urban Change

Who is Rational?

2017 ◽  
Author(s):  
Joseph Ben Prestel
Keyword(s):  
Middle Class ◽  
Lower Class ◽  
Urban Change ◽  
Social Formation ◽  
Court Records ◽  
Local Newspapers ◽  
Dynamic Transformation ◽  
The Social ◽  
Recent Emergence

Beginning around 1860, authors in the Egyptian capital portrayed Cairo’s changing cityscape and the recent emergence of local newspapers in terms of their impact on rationality (‘aql). In their descriptions, these contemporaries depicted rationality as an education of the heart that especially enabled men from the middle class to control their bodies and passions. The chapter shows that Cairo’s transformation was, however, not always associated with rising rationality by drawing on a different set of sources. Police and court records from the 1860s and 1870s demonstrate that contemporaries also described processes of urban change as a danger to the “honor” of lower-class women. Like the debates in Berlin, emotional practices in Cairo thus served as a way to address the social formation of the Egyptian capital during a time of dynamic transformation.

Introduction

2017 ◽  
Author(s):  
Joseph Ben Prestel
Keyword(s):  
Nineteenth Century ◽  
Middle East ◽  
Urban History ◽  
Comparative Studies ◽  
Urban Change ◽  
History Of Emotions ◽  
History Of ◽  
New Perspective

The introduction shows that the historical parallels between cities in Europe and the Middle East during the nineteenth century are an underresearched topic in history, demonstrating that Eurocentric tendencies have led to a separation between historical studies on cities in these two regions. It shows how a comparison between Berlin and Cairo contributes to the study of potential parallels between cities in Europe and the Middle East. It is in this context that the history of emotions opens up a new perspective. While older comparative studies have focused on the origins of urban change, the introduction argues that a history of emotions shifts the focus towards the study of how contemporaries negotiated urban change. In this way, the history of emotions helps to overcome Eurocentric pitfalls and offers the possibility of a more global urban history, in which the histories of Berlin and Cairo begin to speak to each other.

Denver’s Green Mile: Marijuana gentrification as a process of urban change

2021 ◽  
pp. 1-19
Author(s):  
Nick T. Van De Voorde ◽  
Tim Slack ◽  
Michael S. Barton
Keyword(s):  
Urban Change

scholarly journals Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3000
Author(s):  
Georg Zitzlsberger ◽  
Michal Podhorányi ◽  
Václav Svatoň ◽  
Milan Lazecký ◽  
Jan Martinovič
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Urban Change ◽  
Small Scale ◽  
Atmospheric Effects ◽  
Wide Field ◽  
Ground Truth Data ◽  
Automated Method ◽  
Urban Monitoring ◽  
Sar Data

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

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