Understanding human movement and city dynamics has always been challenging. From traditional methods of manually observing the city's inhabitant, to using cameras, to now using sensors and more complex technology, the field of urban monitoring has evolved greatly. Still, there are more that can be done to unlock better practices for understanding city dynamics. This paper surveys how the landscape of urban dynamics studying has evolved with a particular focus on event-based cameras. Event-based cameras capture changes in light intensity instead of the RGB values that traditional cameras do. They offer unique abilities, like the ability to work in low-light, that can make them advantageous compared to other sensors. Through an analysis of event-based cameras, their applications, their advantages and challenges, and machine learning applications, we propose event-based cameras as a medium for capturing information to study urban dynamics. They offer the ability to capture important information while maintaining privacy. We also suggest multi-sensor fusion of event-based cameras and other sensors in the study of urban dynamics. Combining event-based cameras and infrared, event-LiDAR, or vibration has to potential to enhance the ability of event-based cameras and overcome the challenges that event-based cameras have.
We estimate the number of street vendors in New York City. First, we summarize the process by which vendors receive licenses and permits to operate legally in New York City. We then describe a survey that was administered by the Street Vendor Project while distributing coronavirus relief aid to vendors operating in New York City both with and without a license or permit. Finally, we review ratio estimation and develop a theoretical justification based on the theory of point processes. We find approximately 23,000 street vendors operate in New York City: 20,500 mobile food vendors and 2,400 general merchandise vendors. One third are located in just six ZIP Codes: 11368 (16%), 11372 (3%), and 11354 (3%) in North and West Queens and 10036 (5%), 10019 (4%), and 10001 (3%) in the Chelsea and Clinton neighborhoods of Manhattan. Our estimates suggest the American Community Survey misses the majority of New York City street vendors.
This study addresses the challenge of urban safety in New York City by examining the relationship between the built environment and crime rates using machine learning and a comprehensive dataset of street view images. We aim to identify how urban landscapes correlate with crime statistics, focusing on the characteristics of street views and their association with crime rates. The findings offer insights for urban planning and crime prevention, highlighting the potential of environmental design in enhancing public safety.
Language endangerment is a phenomenon in which approximately 40% of languages spoken worldwide are predicted to disappear within the next few decades, resulting in the loss of cultures associated with these languages. To take effective measures against language endangerment, it is essential to quantitatively understand its characteristics because it is a phenomenon in which historical, geographical, and economic factors are intricately intertwined. In this study, multilayer language-country bipartite networks are constructed using information about which countries each language is spoken in and two types of linguistic features, namely the existence of a writing system and the function within a country. In addition, percolation simulations are conducted to measure how language and country networks break down according to the extinction of languages and to identify vulnerable connections in them. In the language network of officially used languages with their writing system, the community analysis indicated that there were communities composed of languages spoken over geographically separated distances. The strength of languages revealed that the official languages in the former colonial nations, namely English, French, Spanish, Dutch, Portuguese, and Russian, still played significant roles in the formation of these communities. In the language and country networks of unofficially used languages without their writing system, the percolation simulation revealed that languages were likely to severely disappear in the Americas, and that linguistic diversity was vulnerable in affluent countries. The findings show that the analysis of multilayer language-country bipartite networks has enabled a quantitative understanding of the language endangerment occurring worldwide from historical, geographical, and economic perspectives.
Although the functional mix of housing and work promises to create compact settlement structures, the impact of job-creating commercial developments on housing demand is not sufficiently managed in current practice. As a result, there is often an imbalance between housing units and labour force, which is articulated in an increased demand for new land take. The authors take this as a starting point to develop a four-stage model, which is the subject of this article. This model seeks to systematically determine the effects of commercial developments with an impact on jobs on the demand for residential space and to provide a basis for the sustainable management of requirements within the framework of spatial planning. In a first step, the labour force moving in is determined by means of rates of regional mobility and the number of households moving to the area is derived. In a second step, existing commuter structures in the inter-communal context are used to estimate how households should ideally be localised; in the third step, households are then distributed mathematically on the basis of accessibility structures. In the fourth and final step, we articulate a proposal for the efficient realization of demand tailored to distinct housing segments. Finally, the model is applied to the Leipzig/Halle region.
Cities. Urban geography, Urbanization. City and country
Abstract Human development is a complex process involving interactions between individuals and their socioeconomic, biological, and physical environments. It has been studied using two frameworks: the “Capabilities Approach,” implemented at the national scale, and the “Neighborhood Effects Approach,” implemented at the community scale. However, no existing framework conceptualizes and measures human development across geographic scales. Here, we unite the two approaches by localizing the Human Development Index (HDI), and demonstrate a methodology for scalable implementation of this index for comparative analysis. We analyzed patterns of development in the United States, characterizing over 70,000 communities. We found that, on average, larger cities have higher HDI (higher standard of living) but exhibit greater disparities between communities, and that increases in community HDI are associated with the simultaneous reduction of a diverse set of negative neighborhood effects. Our framework produces an interdisciplinary synthesis of theory and practice for sustainable, equitable urban health and development.
Most existing point-of-interest (POI) recommenders aim to capture user preference by employing city-level user historical check-ins, thus facilitating users' exploration of the city. However, the scarcity of city-level user check-ins brings a significant challenge to user preference learning. Although prior studies attempt to mitigate this challenge by exploiting various context information, e.g., spatio-temporal information, they ignore to transfer the knowledge (i.e., common behavioral pattern) from other relevant cities (i.e., auxiliary cities). In this paper, we investigate the effect of knowledge distilled from auxiliary cities and thus propose a novel Meta-learning Enhanced next POI Recommendation framework (MERec). The MERec leverages the correlation of check-in behaviors among various cities into the meta-learning paradigm to help infer user preference in the target city, by holding the principle of "paying more attention to more correlated knowledge". Particularly, a city-level correlation strategy is devised to attentively capture common patterns among cities, so as to transfer more relevant knowledge from more correlated cities. Extensive experiments verify the superiority of the proposed MERec against state-of-the-art algorithms.
In the digital era, Extended Reality (XR) is considered the next frontier. However, XR systems are computationally intensive, and they must be implemented within strict latency constraints. Thus, XR devices with finite computing resources are limited in terms of quality of experience (QoE) they can offer, particularly in cases of big 3D data. This problem can be effectively addressed by offloading the highly intensive rendering tasks to a remote server. Therefore, we proposed a remote rendering enabled XR system that presents the 3D city model of New York City on the Microsoft HoloLens. Experimental results indicate that remote rendering outperforms local rendering for the New York City model with significant improvement in average QoE by at least 21%. Additionally, we clarified the network traffic pattern in the proposed XR system developed under the OpenXR standard.
Carmen Cabrera-Arnau, Chen Zhong, Michael Batty
et al.
The polycentric city model has gained popularity in spatial planning policy, since it is believed to overcome some of the problems often present in monocentric metropolises, ranging from congestion to difficult accessibility to jobs and services. However, the concept 'polycentric city' has a fuzzy definition and as a result, the extent to which a city is polycentric cannot be easily determined. Here, we leverage the fine spatio-temporal resolution of smart travel card data to infer urban polycentricity by examining how a city departs from a well-defined monocentric model. In particular, we analyse the human movements that arise as a result of sophisticated forms of urban structure by introducing a novel probabilistic approach which captures the complexity of these human movements. We focus on London (UK) and Seoul (South Korea) as our two case studies, and we specifically find evidence that London displays a higher degree of monocentricity than Seoul, suggesting that Seoul is likely to be more polycentric than London.
Language difference is one of the factors that hinder the acquisition of second language skills. In this article, we introduce a novel solution that leverages the strength of deep neural networks to measure the semantic dissimilarity between languages based on their word distributions in the embedding space of the multilingual pre-trained language model (e.g.,BERT). Then, we empirically examine the effectiveness of the proposed semantic language distance (SLD) in explaining the consistent variation in English ability of countries, which is proxied by their performance in the Internet-Based Test of English as Foreign Language (TOEFL iBT). The experimental results show that the language distance demonstrates negative influence on a country's average English ability. Interestingly, the effect is more significant on speaking and writing subskills, which pertain to the productive aspects of language learning. Besides, we provide specific recommendations for future research directions.
Global population growth and rampant urbanization have led to the accelerated development of large cities, which are themselves rapidly affected by a sudden increase in transport demand, or even a disruption of the daily concerns of citizensand the economic growth of the country. Currently, management of urban transport is a major issue in terms of the quality of life for citizens and the economic, social and cultural competition between the different cities. In many parts of the country, cities are booming, which has caused a major disruption in traffic. In spite of investments and development on urban layout, planning and development of means of transport in many cities of the country are faced with problems of massive influx of users, traffic jams, congestion, traffic accidents, and air and noise pollution. However, it should be mentioned that the problems are not identicaland each city has its own specificities. The proposed working methodology is based on the identification of the problems in the field reported in the various national media and newspapers, grouped into five groups mentioned in the working approach. These are theblack spotsrecorded in some wilayasof the republic, especially the big cities (Algiers, Constantine, Setif...). Knowledge of the magnitude of the factors present on the ground makes it possible to have an overview of the malfunctions, or even perform « mapping of the black spots» to better choose the best directions to take. Thus, this study aims to highlightsomeof theurban transport problems encountered in certain Algerian citiesandto then suggestpossible solutions, or even identify actions to be undertakenon a priority basiscurrently andin thefuture.
Ecological development is essentially the process of building a resource-efficient, environment-friendly and ecologically-safe society, and of achieving modernization that features harmonious coexistence between man and nature. Xi Jinping Thought on Ecological Civilization offers answers to such theoretical and practical questions as why to build an ecological civilization, what the goal is and how to achieve it. Its practice model focuses on explaining the norms, content, paths and methods of building an ecological civilization. It is a structural and operable approach for implementing Xi Jinping Thought on Ecological Civilization and guiding the construction of ecological civilization. As an intermediate link between Xi Jinping Thought on Ecological Civilization and eco-civilization construction, the practice model is not only an indispensable part of the former, but also the key to combine the theory and practice concerning the two.
Urbanization. City and country, Environmental sciences
Both within the United States and worldwide, the city of Detroit has become synonymous with economic decline, depopulation, and crime. Is Detroit's situation unique, or can similar neighborhoods be found elsewhere? This study examines Census block group data, as well as local crime statistics for 2014, for a set of five Midwestern cities. Roughly three percent of Chicago's and Milwaukee's block groups--all of which are in majority nonwhite areas--exceed Detroit's median values for certain crimes, vacancies, and a poverty measure. This figure rises to 11 percent for St. Louis, while Minneapolis has only a single "Detroit-like" block group. Detroit's selected areas are more likely to be similar to the entire city itself, both spatially and statistically, while these types of neighborhoods for highly concentrated "pockets" of poverty elsewhere. Development programs that are targeted in one city, therefore, must take these differences into account and should be targeted to appropriate neighborhoods.
Imam Buchori, Pangi Pangi, Angrenggani Pramitasari
et al.
This study observes the socio-spatial dynamics in the suburbs of a medium-sized city, particularly considering the extent to which shifting land use has influenced people’s welfare. This case study selected suburban Surakarta, a medium-sized metropolitan city in Central Java Province, Indonesia. The methods employed were descriptive statistics and spatial analyses. Considering the data availability, unit of analysis was urban or rural villages ( kelurahan or desa). The results show that the development follows the pattern of a regional network, but the spatial dynamics are quite different in each direction. Besides, the shift of land use from agriculture to urban land has not directly affected poverty reduction. In the study area, the increase in industrial land use showed a weak positive correlation with the addition of pre-prosperous families. On this basis, local governments should pay more attention to the existence of the native residents in developing suburbs so that they are not harmed by the shift in land use from agricultural to developed urban land.
Urban population density provides a good perspective for understanding urban growth and socio-spatial dynamics. Based on sub-district data of the five times of national population censuses in 1964, 1982, 1990, 2000, and 2010, this paper is devoted to making analyses of urban growth and the spatial restructuring of population in the city of Hangzhou, China. Research methods are based on mathematical modeling and field investigation. The modeling result shows that the negative exponential function and the power-exponential function can be well fitted to Hangzhou's observational data of urban density. The negative exponential model reflect the expected state, while the power-exponential model reflects the real state of urban density distribution. The parameters of these models are linearly correlated to the spatial information entropy of population distribution. The density gradient in the negative exponential function flattened in the 1990s and 2000s is closely related to the development of suburbanization. In terms of investigation materials and the changing trend of model parameters, we can reveal the spatio-temporal features of Hangzhou's urban growth. The main conclusions can be reached as follows. The policy of reformation and opening-up and the establishment of a market economy improved the development mode of Hangzhou. As long as a city has a good social and economic environment, it will automatically tend to the optimal state through self-organization.
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts. Specifically, we represent the road layout using a graph where nodes in the graph represent control points and edges in the graph represent road segments. NTG is a sequential generative model parameterized by a neural network. It iteratively generates a new node and an edge connecting to an existing node conditioned on the current graph. We train NTG on Open Street Map data and show that it outperforms existing approaches using a set of diverse performance metrics. Moreover, our method allows users to control styles of generated road layouts mimicking existing cities as well as to sketch parts of the city road layout to be synthesized. In addition to synthesis, the proposed NTG finds uses in an analytical task of aerial road parsing. Experimental results show that it achieves state-of-the-art performance on the SpaceNet dataset.