Right this way: Exploring the use of mobile maps in Street‐Level wayfinding

2020 ◽  
Vol 57 (1) ◽  
Author(s):  
Rebecca Noone
Keyword(s):  
Street Level ◽  
Mobile Maps

The Working Sheep Dog as a Model of the Street- Level Public Servant

Public Voices ◽  
2017 ◽  
Vol 6 (1) ◽  
pp. 51
Author(s):  
Anne J. Hacker
Keyword(s):  
Organizational Structures ◽  
Servant Leader ◽  
Street Level ◽  
Public Servant ◽  
The Relationship

There are examples all around us of natural, simple, yet amazingly complex organizational structures that demonstrate models of leadership that are of use today. The working sheep dog is one such example. It is a vision of grace, ability, stamina and integrity. The relationship that exists between theses canine and human partners mirrors that of the street-level public servant and servant leader.

The Art of Improvisation and Street-Level Bureaucracy

Public Voices ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. 68
Author(s):  
Lauren Bock Mullins
Keyword(s):  
Bureaucratic Discretion ◽  
Street Level ◽  
Similarities And Differences ◽  
New Framework ◽  
Street Level Bureaucracy

This article explores the similarities and differences between the art of improvisation and street-level bureaucracy. By offering a new framework that points out the similarities between bureaucratic discretion and improvisation, we see how street-level bureaucracy has artistic elements, which can be helpful in expanding our understanding of this phenomenon.

Policy Refiners and Street-Level Lesson Learners

2018 ◽  
Author(s):  
Alastair Stark
Keyword(s):  
Policy Evaluation ◽  
Central Government ◽  
Street Level ◽  
Street Level Bureaucrats ◽  
Central Level ◽  
Government Level ◽  
Inquiry Lesson

This chapter explores agents who are influential in terms of inquiry lesson-learning but have not been examined before in inquiry literature. The key argument is that two types of agent—policy refiners and street-level bureaucrats—are important when it comes to the effectiveness of post-crisis lesson-learning. As they travel down from the central government level, street-level actors champion, reinterpret, and reject inquiry lessons, often because those lessons do not consider local capacities. Policy refiners, however, operate at the central level in the form of taskforces, implementation reviews, and policy evaluation processes. These refiners examine potentially problematic inquiry lessons in greater detail in order to determine whether and how they should be implemented. In doing so, these ‘mini-inquiries’ can reformulate or even abandon inquiry recommendations.

scholarly journals Mapping Urban Environmental Performance with Emerging Data Sources: A Case of Urban Greenery and Traffic Noise in Sydney, Australia

2021 ◽  
Vol 13 (2) ◽  
pp. 605
Author(s):  
Zahra Nourmohammadi ◽  
Tanapon Lilasathapornkit ◽  
Mudabber Ashfaq ◽  
Ziyuan Gu ◽  
Meead Saberi
Keyword(s):  
Negative Correlation ◽  
Seasonal Variations ◽  
Environmental Performance ◽  
Traffic Noise ◽  
Time Dependent ◽  
Data Sources ◽  
Street Level ◽  
Urban Greenery ◽  
Key Aspects ◽  
Sydney Australia

Measuring urban environmental performance supports understanding and improving the livability and sustainability of a city. Creating a more livable and attractive environment facilitates a greater shift to active and greener transport modes. Two key aspects, among many others, that determine the environmental performance of an urban area are greenery and noise. This study aims to map street-level greenery and traffic noise using emerging data sources including crowd-sourced mobile phone-based data and street-level imagery data in Sydney, Australia. Results demonstrate the applicability of emerging data sources and the presented advanced techniques in capturing the seasonal variations in urban greenery and time-dependent nature of traffic noise. Results also confirm the presence of a negative correlation between urban greenery and traffic noise.

scholarly journals A deep learning approach to identify unhealthy advertisements in street view images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregory Palmer ◽  
Mark Green ◽  
Emma Boyland ◽  
Yales Stefano Rios Vasconcelos ◽  
Rahul Savani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fast Food ◽  
Social Inequalities ◽  
Social Inequalities In Health ◽  
Street Level ◽  
Street View ◽  
Food Advertisements ◽  
Data Collection Effort ◽  
Deprived Areas

AbstractWhile outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements, which may encourage their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool $${360}^{\circ }$$ 360 ∘ Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 25,349, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th–18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.

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