Geographically Explicit Network Analysis of Urban Living and Working Interaction Pattern in Shenzhen City, South China

Human daily mobility plays an important role in urban research. Commuting of urban residents is an important part of urban daily mobility, especially in working days. However, the characteristic of the mobility network formed by the commuting of urban residents and its impact on the internal structure of the city are still an important work that needs to be explored further. Aiming to study the living–working interaction pattern of meta-populations over urban divisions within cities, a fine-grained dataset of living–working tracking of Shenzhen is curated and used to construct an urban living–working mobility network, and the living–working interaction pattern is analyzed through the community structures of the network. The results show that human daily mobility plays an important role in understanding the formation of urban structure, the administrative divisions of the city affect human daily mobility, and human daily mobility reacts on the formation of urban structure.

Evaluating the Street Greening with the Multiview Data Fusion

Street greening, an indispensable element of urban green spaces, has played an important role in beautifying the environment, alleviating the urban heat island effect, and improving residents’ comfort. Vegetation coverage is a common index used for measuring street greening. However, there are some shortcomings in the traditional evaluation methods of vegetation coverage. Part of the vegetation coverage cannot be determined from a two-dimensional perspective, such as shrubs and green walls. In this paper, the Sentinel-2 image was used to extract the street fractional vegetation cover (SFVC) and the Baidu street view panoramas were used to extract the green view index (GVI). To overcome the lack of a single perspective from the street vegetation coverage evaluation, the above two indices were merged to construct a comprehensive street greening evaluation index (CSGEI). The research area is the Longhua District of Shenzhen city in Southern China. All three indices were divided into five classes using natural breakpoint methods based on previous research experience. The results showed that Baidu street view panoramas could effectively identify shrubs and green walls that were deficient in the Sentinel-2 image. The GVI is a supplement to the street vegetation coverage. The SFVC and GVI were divided into five classes, from L1 to L5 implying a gradual increase in the percentage of the vegetated area. The result has shown that the SFVC was in the L1, accounting for 53.68%. After index merging, the process of accounting for the L1 decreased to 31.29%. The multiperspective integrated CSGEI could comprehensively measure the distribution information of street greening and guide the planning and management of urban green landscapes.

Fractal Characteristic Analysis of Urban Land-Cover Spatial Patterns with Spatiotemporal Remote Sensing Images in Shenzhen City (1988–2015)

Understanding the urban land-cover spatial patterns is of particular significance for sustainable development planning. Due to the nonlinear characteristics related to the spatial pattern for land cover, it is essential to provide a new analysis method to analyze them across remote sensing imagery. This paper is devoted to exploring the fractals and fractal dimension properties of land-cover spatial patterns in Shenzhen city, China. Land-cover information was extracted using a supervised classification method with ArcGIS technology from cloud-free Landsat TM/ETM+/OLI imagery, covering 1988–2015. The box-counting method and the least squares regression method are combined to estimate fractal dimensions of the land-cover spatial pattern. The information entropy was used to verify our fractal dimension results. The results show the fractal dimension changes for each land cover type from 1988 to 2015: (1) the land-cover spatial form of Shenzhen city has a clear fractal structure, but fractal dimension values vary in different land cover types; (2) the fractal dimension of build-up land increases and reaches a stable value, while grassland and cultivated land decrease; The fractal structure of grassland and bare land showed a bifractals trend increasing year by year; (3) the information entropy dimension growth is approaching its maximum capacity before 2011. We integrated the information entropy index and fractal dimension to analyze the complexity in land-cover spatial evolution from space-filling, space balance, and space complexity. It can be concluded that driven by policies, the land-cover spatial form in Shenzhen experienced a process from a hierarchical spatial structure with a low evolution intensity to a higher evolution intensity with multiscale differential development. The fractal dimension has been becoming better through self-organization, and its land resources are reaching the growth limits.

ИНФОРМАТИВНОСТЬ ОТДЕЛЬНЫХ ПАРАМЕТРОВ ДЛЯ СОПОСТАВЛЕНИЯ ЭКОЛОГО-БИОЛОГИЧЕСКОГО СОСТОЯНИЯ ПОЧВ ПРИ ОРГАНИЧЕСКОМ И ТРАДИЦИОННОМ ЗЕМЛЕДЕЛИИ

Приведены результаты исследований информативности 9 микробиологических и биохимических параметров для оценки эколого-биологического состояния почв при органическом земледелии. Информативность параметров определялась по степени корреляционной зависимости с изменением антропогенной нагрузки в ряду: почвы традиционных полей –органических полей – фоновых участков. Исследования, проведенные на землях фермерского хозяйства Рыбнослободского района Республики Татарстан, позволили установить, что высокая и очень высокая корреляция с антропогенной нагрузкой проявилась для 7 параметров на дерново-подзолистых почвах хозяйства и всего для 3 – на серых лесных, средняя – для 1 параметра на дерново-подзолистых почвах и для 3 – на серых лесных. Показано, что наиболее перспективнымипараметрами для эколого-биологической оценки почв являются обилие бактерий рода Azotobacter, активность ферментов инвертазы и уреазы. Библиографические ссылки 1. Вольф В.Г. Статистическая обработка опытных данных. М.: Колос, 1966. 254 с.2. Газеев М.А., Турдалиев А.Т., Низометдинов М.Ш. Роль органических и минеральных удобрений в развитии физиологических групп микроорганизмов в системе севооборота // Современные фундаментальные и прикладные исследования. 2019. №2. С. 9‒12.3. ГОСТ 33980-2016. Продукция органического производства. Правила производства, переработки, маркировки и реализации.4. Даденко Е.В., Казеев К.Ш., Колесников С.И., Казеев К.Ш., Вальков В.Ф. Изменение ферментативной активности почвенных образцов при их хранении // Почвоведение. 2009. №12. С. 1481‒1486.5. Зинченко М.К., Федулова И.Д., Шаркевич В.В. Распространение диазотрофных микроорганизмов в агроландшафтах серой лесной почвы // Владимирский земледелец. 2017. №2. С. 11‒14.6. Игнашев, Н.Е., Рыжих Л.Ю. Влияние систем земледелия на обилие аэробной азотфиксирующей микробиоты // Российский журнал прикладной экологии. 2020. №1. С. 30‒32.7. Казеев К.Ш., Колесников С.И. Биодиагностика почв: методология и методы исследований. Ростов-на-Дону: Изд-во Южного федерального ун-та, 2012. 260 с.8. Кулагина В.И., Сунгатуллина Л.М., Рязанов С.С., Хайруллина А.М., Шагидуллин Р.Р., Рупова Э.Х. Проверка набора параметров для интегральной оценки эколого-биологического состояния почв при органическом земледелии // Ученые записки Крымского федерального университета имени В.И. Вернадского. Биология. Химия. 2021. Т. 7, № 1. С. 90‒102. doi: 10.37279/2413-1725-2021-7-1-90-1029. Aruna Kumari J., Rao P.C., Padmaja G., Madhavi M. Effect of physico-chemical properties on soil enzyme urease activity in some soils of Ranga Reddy district of Telangana State, India// International journal of current microbiology and applied sciences. 2017. V. 6, №11. P. 1708‒1714. https://doi.org/10.20546/ijcmas.2017.611.20610. Shi Z.J., Lu Y., Xu Z.G., Fu S.L. Enzyme activities of urban soils under different land use in the Shenzhen city, China // Plant, soil and environment. 2008. V. 54, №8. P. 341–346. https://doi.org/10.17221/415-PSE.

Landslide Detection from Open Satellite Imagery Using Distant Domain Transfer Learning

Using convolutional neural network (CNN) methods and satellite images for landslide identification and classification is a very efficient and popular task in geological hazard investigations. However, traditional CNNs have two disadvantages: (1) insufficient training images from the study area and (2) uneven distribution of the training set and validation set. In this paper, we introduced distant domain transfer learning (DDTL) methods for landslide detection and classification. We first introduce scene classification satellite imagery into the landslide detection task. In addition, in order to more effectively extract information from satellite images, we innovatively add an attention mechanism to DDTL (AM-DDTL). In this paper, the Longgang study area, a district in Shenzhen City, Guangdong Province, has only 177 samples as the landslide target domain. We examine the effect of DDTL by comparing three methods: the convolutional CNN, pretrained model and DDTL. We compare different attention mechanisms based on the DDTL. The experimental results show that the DDTL method has better detection performance than the normal CNN, and the AM-DDTL models achieve 94% classification accuracy, which is 7% higher than the conventional DDTL method. The requirements for the detection and classification of potential landslides at different disaster zones can be met by applying the AM-DDTL algorithm, which outperforms traditional CNN methods.

A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities

Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance.