This paper describes the conception, development and deployment of a novel HCI system for public participation and decisionmaking. This system was applied for the process of allocating refugee accommodation in the City of Hamburg within the FindingPlaces project (FP) in 2016. The CityScope (CS) -a rapid prototyping platform for urban planning and decision-making -offered a technical solution which was complemented by a workshop process to facilitate effective interaction of multiple participants and stakeholder groups. This paper presents the origins of CS and the evolution of the tangible user interface approach to urban planning and public participation. It further outlines technical features of the system, including custom hardware and software in use, utilization in real-time as well as technical constraints and limitations. Special focus is on the adaptation of the CS technology to the specific demands of Hamburg´s FP project, whose procedures, processes, and results are reflected. The final section analyzes success factors as well as shortcomings of the approach, and indicates further R&D as well as application scenarios for the CS.
A fundamental aspect of well performing cities is successful public spaces. For centuries, understanding these places has been limited to sporadic observations and laborious data collection. This study proposes a novel methodology to analyze citywide, discrete urban spaces using highly accurate anonymized telecom data and machine learning algorithms. Through superposition of human dynamics and urban features, this work aims to expose clear correlations between the design of the city and the behavioral patterns of its users. Geolocated telecom data, obtained for the state of Andorra, were initially analyzed to identify "stay-points"-events in which cellular devices remain within a certain roaming distance for a given length of time. These stay-points were then further analyzed to find clusters of activity characterized in terms of their size, persistence, and diversity. Multivariate linear regression models were used to identify associations between the formation of these clusters and various urban features such as urban morphology or land-use within a 25-50 meters resolution. Some of the urban features that were found to be highly related to the creation of large, diverse and long-lasting clusters were the presence of service and entertainment amenities, natural water features, and the betweenness centrality of the road network; others, such as educational and park amenities were shown to have a negative impact. Ultimately, this study suggests a "reversed urbanism" methodology: an evidence-based approach to urban design, planning, and decision making, in which human behavioral patterns are instilled as a foundational design tool for inferring the success rates of highly performative urban places.
Throughout the COVID-19 pandemic, nonpharmaceutical interventions, such as mobility restrictions, have been globally adopted as critically important strategies to curb the spread of infection. However, such interventions come with immense social and economic costs and the relative effectiveness of different mobility restrictions are not well understood. Some recent works have used telecoms data sources that cover fractions of a population to understand behavioral changes and how these changes have impacted case growth. This study analyzed uniquely comprehensive datasets in order to examine the relationship between mobility and transmission of COVID-19 in the country of Andorra. The data consisted of spatiotemporal telecoms data for all mobile subscribers in the country, serology screening results for 91% of the population, and COVID-19 case reports. A comprehensive set of mobility metrics was developed using the telecoms data to indicate entrances to the country, contact with tourists, stay-at-home rates, trip-making and levels of crowding. Mobility metrics were compared to infection rates across communities and transmission rate over time. All metrics dropped sharply at the start of the country's lockdown and gradually rose again as the restrictions were gradually lifted. Several of these metrics were highly correlated with lagged transmission rate. There was a stronger correlation for measures of indoor crowding and inter-community tripmaking, and a weaker correlation for total trips (including intra-community trips) and stay-at-homes rates. These findings provide support for policies which aim to discourage gathering indoors while lifting the most restrictive mobility limitations.
Urban density, in the form of residents’ and visitors’ concentration, is long considered to foster diverse exchanges of interpersonal knowledge and skills, which are intrinsic to sustainable human settlements. However, with current urban studies primarily devoted to city and district-level analysis, we cannot unveil the elemental connection between urban density and diversity. Here we use an anonymized and privacy-enhanced mobile data set of 0.5 million opted-in users from three metropolitan areas in the U.S. to show that at the scale of urban streets, density is not the only path to diversity. We represent the diversity of each street with the Experienced Social Mixing (ESM), which describes the chances of people meeting diverse income groups throughout their daily experience. We conduct multiple experiments and show that the concentration of visitors only explains 26% of street-level ESM. However, adjacent amenities, residential diversity, and income level account for 44% of the ESM. Moreover, using longitudinal business data, we show that streets with an increased number of food businesses have seen an increased ESM from 2016 to 2018. Lastly, although streets with more visitors are more likely to have crime, diverse streets tend to have fewer crimes. These findings suggest that cities can leverage many tools beyond density to curate a diverse and safe street experience for people.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.