scholarly journals Where to put bike counters? Stratifying bicycling patterns in the city using crowdsourced data

2019 ◽  
Author(s):  
Vanessa Brum-Bastos ◽  
Colin J. Ferster ◽  
Trisalyn Nelson ◽  
Meghan Winters
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Urban Areas ◽  
Representative Sample ◽  
Temporal Patterns ◽  
Continuous Signal ◽  
Crowdsourced Data ◽  
The City ◽  
Street Segments ◽  
Daily Patterns

When designing bicycle count programs, it can be difficult to know where to locate counters to generate a representative sample of bicycling ridership. Crowdsourced data on ridership has been shown to represent patterns of temporal ridership in dense urban areas. Here we use crowdsourced data and machine learning to categorize street segments into classes of temporal patterns of ridership. We used continuous signal processing to group 3,880 street segments in Ottawa, Ontario into six classes of temporal ridership that varied based on overall volume and daily patterns (commute vs non-commute). Transportation practitioners can use this data to strategically place counters across these strata to efficiently capture bicycling ridership counts that better represent the entire city.

Quantifying changes in bicycle volumes using crowdsourced data

2022 ◽  
pp. 239980832110661
Author(s):  
Ali Al-Ramini ◽  
Mohammad A Takallou ◽  
Daniel P Piatkowski ◽  
Fadi Alsaleem
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Crowdsourced Data ◽  
Machine Learning Approach ◽  
Bicycle Infrastructure ◽  
The Difference ◽  
Infrastructure Investments ◽  
Using Data ◽  
The Impact ◽  
The City

Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.

scholarly journals Clasificación espacial del suelo urbano por el valor especulativo del suelo e imágenes MSI satelitales usando K-MEANS, Huancayo, Perú

2021 ◽  
Vol 24 (44) ◽  
pp. 70-83
Author(s):  
Gonzalo Rodolfo Peña-Zamalloa
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Land Use Changes ◽  
Land Market ◽  
Objective Classification ◽  
Urban Land Market ◽  
The City ◽  
Sentinel 2 ◽  
Urban Territory

The city of Huancayo, like other intermediate cities in Latin America, faces problems of poorly planned land-use changes and a rapid dynamic of the urban land market. The scarce and outdated information on the urban territory impedes the adequate classification of urban areas, limiting the form of its intervention. The purpose of this research was the adoption of unassisted and mixed methods for the spatial classification of urban areas, considering the speculative land value, the proportion of urbanized land, and other geospatial variables. Among the data collection media, Multi-Spectral Imagery (MSI) from the Sentinel-2 satellite, the primary road system, and a sample of direct observation points, were used. The processed data were incorporated into georeferenced maps, to which urban limits and official slopes were added. During data processing, the K-Means algorithm was used, together with other machine learning and assisted judgment methods. As a result, an objective classification of urban areas was obtained, which differs from the existing planning.

scholarly journals POSSIBILITIES OF MACHINE LEARNING METHODS FOR FORECASTING THE DEVELOPMENT OF URBAN TERRITORIES

2020 ◽  
Vol 6 (2) ◽  
pp. 77-81
Author(s):  
Andrey A. Sivkov ◽  
Alexey A. Kolesnikov
Keyword(s):  
Machine Learning ◽  
Urban Design ◽  
Spatial Modeling ◽  
Urban Areas ◽  
Spatial Process ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Territorial Planning ◽  
Urban Territories ◽  
The City

Urban design has always been a spatial process. Since the city is a combination of spaces and connections, both above and under ground, it is especially important to bring territorial planning to the level of spatial modeling. In the paper, the possibilities of machine learning methods for predicting the development of urban areas were investigated, a forecasting model was compiled, and its accuracy was evaluated.

scholarly journals What Urban Cameras Reveal About the City: The Work of the Senseable City Lab

2021 ◽  
pp. 491-502
Author(s):  
Fábio Duarte ◽  
Carlo Ratti
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Urban Areas ◽  
Urban Landscape ◽  
Urban Life ◽  
Machine Learning Techniques ◽  
Making Sense ◽  
Learning Techniques ◽  
Responsive Environments ◽  
The City

AbstractCameras are part of the urban landscape and a testimony to our social interactions with city. Deployed on buildings and street lights as surveillance tools, carried by billions of people daily, or as an assistive technology in vehicles, we rely on this abundance of images to interact with the city. Making sense of such large visual datasets is the key to understanding and managing contemporary cities. In this chapter, we focus on techniques such as computer vision and machine learning to understand different aspects of the city. Here, we discuss how these visual data can help us to measure legibility of space, quantify different aspects of urban life, and design responsive environments. The chapter is based on the work of the Senseable City Lab, including the use of Google Street View images to measure green canopy in urban areas, the use of thermal images to actively measure heat leaks in buildings, and the use of computer vision and machine learning techniques to analyze urban imagery in order to understand how people move in and use public spaces.

Urbanization of fauna on the example of marsh frog (Pelophylax ridibundus)

2015 ◽  
Vol 26 (3-4) ◽  
pp. 116-123
Author(s):  
A. P. Korzh ◽  
T. V. Zahovalko
Keyword(s):  
Urban Areas ◽  
Cognitive Modeling ◽  
Negative Impact ◽  
Natural Populations ◽  
Cellular Level ◽  
Anthropogenic Factors ◽  
Migration Routes ◽  
Corrective Effect ◽  
The Impact ◽  
The City

Recently, the number of published works devoted to the processes of synanthropization of fauna, is growing like an avalanche, which indicates the extreme urgency of this theme. In our view, the process of forming devices to coexist with human and the results of his life reflects the general tandency of the modern nature evolution. Urbanization is characteristic for such a specific group of animals like amphibians, the evidence of which are numerous literature data. Many researchers use this group to assess the bioindicative quality of the environment. For this aim a variety of indicators are used: from the cellular level of life of organization up to the species composition of the group in different territories. At the same time, the interpretation of the results is not always comparable for different areas and often have significantly different interpretations by experts. Urban environment, primarily due to the contamination is extremely aggressive to amphibians. As a consequence, the urban populations of amphibians may be a change in the demographic structure, affecting the reproductive ability of the population, the disappearance of the most sensitive species or individuals, resizing animals, the appearance of abnormalities in the development, etc. At the same time play an important amphibians in the ecosystems of cities, and some species in these conditions even feel relatively comfortable. Therefore, it is interesting to understand the mechanisms of self-sustaining populations of amphibians in urban environments. To assess the impact of natural and anthropogenic factors on the development of amphibian populations were used cognitive modeling using the program Vensim PLE. Cognitive map of the model for urban and suburban habitat conditions were the same. The differences concerned the strength of connections between individual factors (migration, fertility, pollution) and their orientation. In general, factors like pollution, parasites, predators had negative impact on the population, reducing its number. The birth rate, food and migration contributed to raising number of individuals. Some of the factors affected on the strength to of each other as well: the majority of the factors affected the structure of the population, had an influence on the fertility. Thanks to it the model reflects the additive effect of complex of factors on the subsequent status of the population. Proposed and analyzed four scenarios differing strength and duration of exposure. In the first scenario, a one-time contamination occurs and not subsequently repeated. The second and third scenario assumes half board contamination, 1 year (2 scenario) and two years (scenario 3). In the fourth scenario, the pollution affected the population of amphibians constantly. In accordance with the results of simulation, much weaker than the natural populations respond to pollution - have them as an intensive population growth and its disappearance at constant pollution is slow. Changes to other parameters of the model showed that this pollution is the decisive factor -only the constant action leads to a lethal outcome for the populations. All other components of the model have a corrective effect on the population dynamics, without changing its underlying trand. In urban areas due to the heavy impact of pollution maintaining the population is only possible thanks to the migration process – the constant replenishment of diminishing micropopulations of natural reserves. This confirms the assumption that the form of existence metapopulations lake frog in the city. In order to maintain the number of amphibians in urban areas at a high level it is necessary to maintain existing migration routes and the creation of new ones. Insular nature of the placement of suitable habitats in urban areas causes the metapopulation structure of the types of urbanists. Therefore, the process of urbanization is much easier for those species whicht are capable of migration in conditions of city. In the initial stages of settling the city micropopulationis formed by selective mortality of the most susceptible individuals to adverse effects. In future, maintaining the categories of individuals is provided mainly due to migration processes metapopulisation form of the species of existence is supported). It should be noted that the changes in the previous levels are always saved in future. In the case of reorganizations of individuals we of morphology can assume the existence of extremely adverse environmental conditions that threaten the extinction of the micropopulations. 

Epizootological features of demodicosis in homeless animals in the city of Tyumen ЭПИЗООТОЛОГИЧЕСКИЕ ОСОБЕННОСТИ ДЕМОДЕКОЗА У БЕЗДОМНЫХ ЖИВОТНЫХ В ГОРОДЕ ТЮМЕНИ

2019 ◽  
pp. 6-11
Keyword(s):  
Clinical Manifestation ◽  
Seasonal Dynamics ◽  
Urban Areas ◽  
Entire Population ◽  
Potential Threat ◽  
Stray Dogs ◽  
Stray Cats ◽  
The City

Широкое распространение безнадзорных животных на территории городов несет за собой потенциальную угрозу распространения зооантропонозных заболеваний, одним из которых является демодекоз. Невозможно разработать мероприятия, направленные на борьбу с заболеванием и его профилактику, без анализа данных особенностей возникновения и распространения инвазии среди всей популяции восприимчивых животных. Поэтому целью нашей работы явилось изучение распространения демодекоза среди безнадзорных собак и кошек в городе Тюмени. В задачи исследования входило изучение распространения демодекоза и его клинического проявления среди бездомных собак и кошек в условиях города Тюмени и определение сезонной динамики заболевания. Работу выполняли в 2016-2018 гг. на базе кафедры анатомии и физиологии ФГБОУ ВО ГАУ Северного Зауралья, в лаборатории акарологии ВНИИВЭА – филиала ТюмНЦ СО РАН, а также в производственных условиях на базе пункта временного содержания безнадзорных домашних животных МКУ «ЛесПаркХоз». Демодекозная инвазия распространена среди бездомных кошек и собак. Наиболее часто демодекоз встречается у собак, экстенсивность инвазии от 0,65 до 0,72%. Заболевание демодекозом у бездомных собак регистрировали на протяжении всего года, но 54,6% больных собак поступали в апреле и мае. Большинство больных демодекозом – это молодые собаки в возрасте от 1,5 месяцев до 2-х лет – 75,76%, животные старше двух лет гораздо реже страдали от демодекоза – 24,24%. Генерализацию демодекоза регистрировали у 21 собаки (63,64%), а локализованные очаги – у 12 собак (36,36%). Наиболее распространенной формой проявления демодекоза у бездомных собак является пустулезная, или пиодемодекоз. Данная форма заболевания была отмечена у 16 собак (48,49%), чешуйчатая форма отмечалась у 10 собак (30,30%), а смешанная – у 7 собак (21,21%). The widespread use of stray animals in urban areas carries with it the potential threat of the spread of zooanthroponotic diseases, one of which is demodicosis. It is impossible to develop measures aimed at combating the disease and its prevention without analyzing the data on the characteristics of the occurrence and spread of invasion among the entire population of susceptible animals. Therefore, the purpose of our work was to study the distribution of demodicosis among street dogs and cats in the city of Tyumen. The objectives of the study included the study of the spread of demodicosis and its clinical manifestation among stray dogs and cats in the conditions of the city of Tyumen and the determination of the seasonal dynamics of the disease. Demodectic invasion is common among stray cats and dogs. Most often, demodicosis occurs in dogs, with extensive invasion from 0.65 to 0.72%. Demodecosis in stray dogs was recorded throughout the year, but 54.6% of sick dogs were reported in April and May. The majority of patients with demodicosis are young dogs between the ages of 1.5 months and 2 years old - 75.76%, animals older than two years suffer less from demodicosis, only 24.24%. Generalization of demodicosis was recorded in 21 dogs (63.64%), and localized foci in 12 dogs (36.36%). The most common form of demodicosis in stray dogs is pustular or pyodemodecosis. This form of the disease was observed in 16 dogs (48.49%), scaly form, was observed in 10 dogs (30.30%), and mixed in 7 dogs (21.21%).

PHYTOMONITORING AS A METOD OF THE ASSESSMENT OF ATMOSPHERIC AIR POLLUTION BY URBAN ENVIRONMENT BY FINE DUST

2019 ◽  
pp. 42-53
Author(s):  
Irina Glinyanova ◽  
Valery Azarov ◽  
Valery Fomichev
Keyword(s):  
Urban Environment ◽  
Urban Areas ◽  
Fine Particles ◽  
Prunus Armeniaca ◽  
Dust Particles ◽  
Fine Dust ◽  
Control Zone ◽  
Atmospheric Air ◽  
Random Functions ◽  
The City

Fine dust: (PM2.5, PM10) is a priority pollutant that contributes to the development of numerous dis-eases in urban areas. The purpose of this scientific work is to study the dispersed composition of dust parti-cles on the leaves of apricot trees (Prúnus armeníaca) in the residential zone of Volgograd. The novelty of the work lies in the study of the dispersed composition of dust particles on the leaves of apricot trees (Prúnus armeníaca) in the residential zone in the city of Volgograd near the construction industry enterprise, me-chanical engineering, leather production and railway transport line in comparison with the conditionally clean (control) zone of the SNT “Orocenets” ”(Sovetsky District, Volgograd) from the standpoint of random functions expressed by integral distribution curves of the mass of particles over their equivalent diameters. As a result of the research, the dispersed composition of dust on the leaves of apricot trees (Prúnus ar-meníaca) in the residential area of Volgograd was revealed. Fine particles were found: PM2.5, PM10 in each of the studied points, which by their values, both in their number and mass fraction, significantly exceed the data on fine dust in a conditionally clean area (control) in the SNT “Oroshanets” (Sovetsky district Volgo-grad), which creates certain environmental risks for local residents. The dispersed analysis of particles from the standpoint of random functions in the future will allow with a sufficiently high degree of accuracy to pre-dict the dust content of urban atmospheric air in the range of monthly and / or seasonal average values compared to the traditional measurement of fine dust concentration in atmospheric air of the urban environ-ment as the maximum single or daily average. At the same time, further studies of dust on the leaves of plants in an urban environment, namely, the study of the density of its sedimentation, will also reveal a group of ur-ban plants that are best suited to retain PM2.5 and PM10 on leaf plates in this region, which can significantly increase the quality of the atmospheric air of the urban environment and be of a recommendatory nature for the state-owned landscaping services of the city of Volgograd when improving the green areas of a megacity.

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