The growing number of bike sharing systems (BSS) in many cities largely facilitates biking for transportation and recreation. Most recent bike sharing systems produce time and location specific data, ...which enables the study of travel behavior and mobility of each individual. However, despite a rapid growth of interest, studies on massive bike sharing data and the underneath travel pattern are still limited. Few studies have explored and visualized spatiotemporal patterns of bike sharing behavior using flow clustering, nor examined the station functional profiles based on over-demand patterns. This study investigated the spatiotemporal biking pattern in Chicago by analyzing massive BSS data from July to December in 2013 and 2014. The BSS in Chicago gained more popularity. About 15.9% more people subscribed to this service. Specifically, we constructed bike flow similarity graph and used fastgreedy algorithm to detect spatial communities of biking flows. By using the proposed methods, we discovered unique travel patterns on weekdays and weekends as well as different travel trends for customers and subscribers from the noisy massive amount data. In addition, we also examined the temporal demands for bikes and docks using hierarchical clustering method. Results demonstrated the modeled over-demand patterns in Chicago. This study contributes to offer better knowledge of biking flow patterns, which was difficult to obtain using traditional methods. Given the trend of increasing popularity of the BSS and data openness in different cities, methods used in this study can extend to examine the biking patterns and BSS functionality in different cities.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Global warming and rapid urbanization in China have caused a series of ecological problems. One consequence has involved the degradation of lake water environments. Lake surface water temperatures ...(LSWTs) significantly shape water ecological environments and are highly correlated with the watershed ecosystem features and biodiversity levels. Analysing and predicting spatiotemporal changes in LSWT and exploring the corresponding impacts on water quality is essential for controlling and improving the ecological water environment of watersheds. In this study, Dianchi Lake was examined through an analysis of 54 water quality indicators from 10 water quality monitoring sites from 2005 to 2016. Support vector regression (SVR), Principal Component Analysis (PCA) and Back Propagation Artificial Neural Network (BPANN) methods were applied to form a hybrid forecasting model. A geospatial analysis was conducted to observe historical LSWTs and water quality changes for Dianchi Lake from 2005 to 2016. Based on the constructed model, LSWTs and changes in water quality were simulated for 2017 to 2020. The relationship between LSWTs and water quality thresholds was studied. The results show limited errors and highly generalized levels of predictive performance. In addition, a spatial visualization analysis shows that from 2005 to 2020, the chlorophyll-a (Chla), chemical oxygen demand (COD) and total nitrogen (TN) diffused from north to south and that ammonia nitrogen (NH3-N) and total phosphorus (TP) levels are increases in the northern part of Dianchi Lake, where the LSWT levels exceed 17°C. The LSWT threshold is 17.6–18.53°C, which falls within the threshold for nutritional water quality, but COD and TN levels fall below V class water quality standards. Transparency (Trans), COD, biochemical oxygen demand (BOD) and Chla levels present a close relationship with LSWT, and LSWTs are found to fundamentally affect lake cyanobacterial blooms.
Display omitted ➢Spatiotemporal distribution and relationship of LSWT and Chla.➢Lake surface water temperature and water quality threshold in the Dianchi Lake watershed.
•A long term LWST and water quality prediction algorithm was proposed.•Spatiotemporal distribution of LSWT and water quality has been studied.•The LSWT impact on water quality has been revealed.•LSWT is a fundamental cause of lake cyanobacterial bloom of Dianchi Lake.
► Urban sprawl resulted in the loss of green space, particularly in the agriculture land while greening policies contributed to the recovery of grass land. ► Spatial variances in landscape patterns ...were detected in different concentric belts and directional transects. ► Change intensity varied among different types of green space in the context of urbanization and greening policies.
Rapid urbanization has caused many environmental impacts associated with the reduction of green space. Having realized the important role of green space in urban ecosystems, many local governments in China have set out a series of policies to introduce green elements into urban areas. Insights into how urban green space changes in response to urbanization and greening policies are essential for guiding sustainable urban development. This paper employed integrated approaches to characterize the changing patterns and intensities of green space in Kunming, China from 1992 to 2009. Spatial variations of green space pattern were derived through concentric and directional landscape analyses integrated with landscape metrics. Change intensities of the two time periods from 1992 to 2000 and from 2000 to 2009 were calculated for the study area as a whole, the concentric belts, and the directional transects to examine the variation of the green space change rate in the city. Results revealed that both rapid urbanization and greening policies accounted for the process of green space change. Among the green space land use types, agriculture land was largely encroached and fragmented by urban sprawl, especially in the outer belts of the city. Forest land was also impacted but encountered a relatively moderate loss rate compared to agriculture land. Conversely, greening policies contributed to the recovery of grass land in the last decade. The study demonstrated the usefulness of the concentric and directional landscape analyses in characterizing the spatial–temporal variations of urban green space in cities with a concentric development form.
Lake surface water temperature (LSWT) is an important factor of water ecological environment. In the context of global warming, the LSWT of global lakes generally reveals an upward trend. With a ...continuous intensification of human activities and a rapid expansion of the impervious surface, urbanization has exerted an increasing impact on the environment, so the impact of human activities on LSWT cannot be ignored. Because of the special geographical location, the change of LSWT in plateau lakes has important impacts on climate diversity, biodiversity, and cultural diversity. As a result, it is critical to monitor and model the variation characteristics of LSWT in the plateau area. Based on the data set of natural factors representing climate change and human factors representing human activities, this study proposes a classification of lake types by K‐Means clustering method. At watershed scale, 11 lakes in the study area are divided into three types: Natural Lake, Semi‐urban Lake, and Urban Lake (UL). Based on this classification, the variation characteristics of LSWT for the eleven lakes from 2001 to 2017 are analyzed. The causal relationship and contribution of climate change and human activities to the rise of LSWT are discussed. Results show that (1) from 2001 to 2017, the annual mean of LSWT‐day/night and near‐surface air temperature in the 11 lakes show a warming trend, a significant correlation (R = 0.82, α = 0.0164 < 0.5) and a same periodicity, which indicates that near‐surface air temperature is one of the main influencing factors of LSWT warming in Yunnan‐Guizhou Plateau. (2) LSWT warming trend of UL is more obvious than those of Semi‐urban Lake and Natural Lake, indicating that human activities have more significant impact on LSWT of UL. The main driving factors are the impervious surface expansion and population increase. (3) The influence of human activities on the LSWT in Yunnan‐Guizhou Plateau is becoming more and more significant, and it is also the main factor in causing the deterioration of lake water environment in Yunnan‐Guizhou Plateau.
Key Points
Lakes are divided into Natural Lake, Semi‐urban Lake, and Urban Lake by K‐Means clustering algorithm
LSWT warming rate is dependent on combinations of NSAT and human activities; the average rate of LSWT increased is faster than the rate at which air temperatures increased
Human activity will become the main driving factor of LSWT increasing in the research area
Easy access to green space and the presence of lush tree canopy in neighborhoods provide substantial psychophysical benefits to residents. However, these urban amenities are often unevenly ...distributed between white and racial/ethnic minority residents. In this study, we investigated racial/ethnic disparities in access to parks and tree canopy using a geographic information system (GIS) and remote-sensing techniques in six Illinois cities. An accessibility index based on a new Google Maps application programming interface (API) was used to calculate walking distances between points of origins and parks, and integrated classification techniques were applied to calculate the amount of tree canopy. Kernel-smoothing function was applied to both canopy and park layers to transform point value to continuous surface value. Both ordinary regression and spatial regression were used to find the relationship.
The results of this study show that racial/ethnic minorities have less tree canopy in their neighborhoods, but it did not find significant differences in terms of access to parks. Spatial regression was determined to be an effective modeling approach for the data used in this study. Methods used in this study can be extended to study accessibility to various destinations using different means of transit, and the results can guide intervention programs to help reduce environmental inequity.
The number of geo-tagged digital photos has grown exponentially in the past decades. Increasing numbers of digital photos with geo-tags are available on many photo-sharing websites such as Flickr and ...Instagram. The proliferation of online photos offers great opportunities to study people's travel experiences and preferences. Mining tourists' behavior and city preferences has become popular in recent geographic information system (GIS) research. However, the huge amount of data also poses challenges in spatial analytics. In this study, we automate the detection of places of interest in multiple cities based on spatial and temporal features of Flickr images from 2007 on. We also speed up the process by running jobs on top of the RHadoop platform. This project provides fast and accurate tourist destination detection by mining large amounts of geo-tagged Flickr images. In addition, this study provides insight in applying the RHadoop platform to strengthen large geospatial data analytics. Our methods can be applied to many other cities, and results are valuable for tourism management.
•This paper effectively detected and ranked popular tourism destinations in multiple cities.•This paper also leveraged cloud computing to expedite Flickr tag processing and similarity graph preparation.•Computation speed for single machine, multiple threads, and cloud platform were compared for different amount of data.
Chinese urbanization has drawn widespread attention since the 21st century. Understanding urban expansion at a watershed scale including cities of different sizes is important for improving our ...current knowledge of the urban extent and its impact on the hydrological cycle, water management, surface energy balances, and biodiversity. Impervious surface area (ISA) can be used as a synthesized quantifiable index to reflect the intensity of natural ecosystems changing into urban ecosystems. It is important to understand ISA patterns and characteristics, which requires long-term impervious surface data at a high spatial and temporal resolution. Previous methods of ISA estimation mainly focused on the spectral differences between ISA and other land covers, and most studies were inclined to use one or a few images without fully considering the long time series of the temporal domain of the reflective data on remote-sensing images. This assessed the Dianchi Lake watershed as a case study area to illustrate ISA change characteristics in the context of natural and cultural conditions. Firstly, more than two hundred Landsat images (from 1988 to 2017) were downloaded through the United States Geological Survey (USGS) online portal. Secondly, the improved normalized difference build-up index (INDBI) and linear spectral mixture analysis (LSMA) algorithm were combined to apply the method to a series of ISA maps of the Dianchi Lake watershed at an annual resolution. Thirdly, ISA extent characteristics of the Dianchi Lake watershed were analysed from trend and pattern aspects. The results show that the proposed method was highly reliable for detecting and characterizing change, with an extracted ISA accuracy of 92.51%, based on a sample of independent validation points. The Dianchi Lake watershed has begun to adopt 'Rashly Advancing' and 'Great Leap Forward' strategies of urbanization.
High concentration of fine particulate matter (PM2.5) has been shown to be a major contributor to haze weather, which has been associated with an increased prevalence in lung cancer. An accurate ...estimation and predication of PM2.5 historical levels, and its spatial-temporal variability can assist in strategically improving regional air quality and reducing its harmful effects on population health. This paper targets Beijing, Tianjin, and Hebei province (BTH), three northeast province of china (TNPC), Yangtze river delta (YRD) and pearl river delta (PRD) as the study areas. Data used in this study include PM2.5 measurements from April 2013 to December 2016, MODIS AOD raster imageries and five meteorological factors from 2000 to 2016. By combining back propagation artificial neural network (BPANN) and ε-support vector regression (ε-SVR), a novel hybrid model was constructed to impute the historical PM2.5 missing values in the long time series from 2000 to 2012, and to predict the concentration of PM2.5 from April 2014 to December 2017. The hybrid model produced results superior to BPANN and ε-SVR with a higher accuracy, lower error rate, and a stable performance. This model can be applied to the other four regions with consistent results. Results of spatial-temporal analysis indicated that the PM2.5 concentration has increased along with a pollution range expansion in BTH from 2000 to 2010. In addition, the PM2.5 concentration decreased slowly in PRD. The concentration and pollution range of PM2.5 in TNPC and YRD showed a stable trend. In 2012, the four research areas all showed decreased trend, and the pollution range narrowed. From 2013 to 2016, the PM2.5 concentration increased shortly then decreased; in particular, the high pollution areas saw a decrease in PM2.5 concentration, which correlated with control measures adopted by the state during the same time period. The hot spots of PM2.5 were mainly distributed in the inland cities.
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•A novel hybrid model to estimate historically PM2.5 concentration was proposed.•The hybrid BPANN&ε-SVR model outperforms others single model.•Spatial-temporal change characteristics of PM2.5 in four study areas were revealed.
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•Anthropogenic impact has warming effect on the LSWT.•When human activities cause a 1℃ increase in LSWT, the overall average increase in LSWT is 0.24℃.•The impact of human social ...policies on the surface water temperature of lakes will continue.
In the past 40 years, the surface water temperature of lakes worldwide has generally shown an upward trend, with a significant spatial heterogeneity. Previous studies generally attributed the change in lake surface water temperature to direct impact of climate change. However, few studies have explored the potential impact of additional anthropogenic factors, resulting in an incomplete understanding of the anthropogenic influence on lake surface water temperatures. We thus propose a new method to quantify the anthropogenic impact on the surface water temperature of lakes. We selected 11 lakes characterized by significant variations in the intensity of watershed urbanization development on the Yunnan-Guizhou Plateau in China for an empirical study, and discussed the trend of LSWT (lake surface water temperature) changes under the anthropogenic impact, as well as the potential link between these changes and human social policies. The research results show that (1) The mean annual rate of change in LSWT due to anthropogenic impact fluctuates of 0.06℃ per year. (2) LSWT is sensitive to changes in anthropogenic activities; a 1℃ increase in LSWT due to anthropogenic factors typically results in a mean temperature variation of 0.24℃. (3) During the three years of the COVID-19 pandemic, LSWT on the Yunnan-Guizhou Plateau was significantly affected by anthropogenic activities.
The boreal forests, identified as a critical "tipping element" of the Earth's climate system, play a critical role in the global carbon budget. Recent findings have suggested that terrestrial carbon ...sinks in northern high-latitude regions are weakening, but there has been little observational evidence to support the idea of a reduction of carbon sinks in northern terrestrial ecosystems. Here, we estimated changes in the biomass carbon sink of natural stands throughout Canada's boreal forests using data from long-term forest permanent sampling plots. We found that in recent decades, the rate of biomass change decreased significantly in western Canada (Alberta, Saskatchewan, and Manitoba), but there was no significant trend for eastern Canada (Ontario and Quebec). Our results revealed that recent climate change, and especially drought-induced water stress, is the dominant cause of the observed reduction in the biomass carbon sink, suggesting that western Canada's boreal forests may become net carbon sources if the climate change–induced droughts continue to intensify.
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