中国社会经济与环境协调发展的可持续减贫模式

过去几十年里,中国在贫困地区实施了大量的减贫措施,贫困地区的基础设施和居民的生活水平得到了极大地改善.中国的贫困地区与生态保护区和脆弱区高度重叠,而贫困地区社会经济发展与环境之间的协调关系仍然没有得到充分认识.本研究提出了一个包含社会经济和生态环境维度的区县水平指标体系,揭示了贫困地区社会经济和环境的时空变化模式,并评估了它们之间的协调发展关系.研究表明,2000-2020年,中国贫困地区社会经济和环境的协调发展指数得到了加强.同时,在社会经济指数以58.4%的增长率实现增长的同时,环境指数也有明显改善,增长率为19.6%.贫困地区在社会经济发展方面与非贫困地区仍然有较大差距,但贫困地区在环境指数方面表现较好.得益于中国的地理多样性,本文确定了有利有效性、持续性、周期性和有限有效性四种减贫模式,可以为世界其他地区在应对减贫和环境保护方面的挑战提供参考.

Revisiting the gravity laws of inter-city mobility in megacity regions

Inter-city mobility is one of the most important issues in the UN Sustainable Development Goals,as it is essential to access the regional labour market,goods and services,and to constrain the spread of infectious diseases.Although the gravity model has been proved to be an effective model to describe mobility among settlements,knowledge is still insufficient in regions where dozens of megacities interact closely and over 100 million people reside.In addition,the existing knowledge is limited to overall population mobility,while the difference in inter-city travel with different purposes is unexplored on such a large geographic scale.We revisited the gravity laws of inter-city mobility using the 2.12 billion trip chains recorded by 40.48 million mobile phone users’trajectories in the Jing-Jin-Ji Region,which contains China’s capital Beijing.Firstly,unlike previous studies,we found that non-commuting rather than commuting is the dominant type of inter-city mobility(89.3%).Noncommuting travellers have a travel distance 42.3%longer than commuting travellers.Secondly,we developed more accurate gravity models for the spatial distribution of inter-city commuting and non-commuting travel.We also found that inter-city mobility has a hierarchical structure,as the distribution of inter-city travel volume follows Zipf’s law.In particular,the hierarchy of non-commuting travel volume among the cities is more in line with an ideal Zipf distribution than commuting travel.Our findings contribute to new knowledge on basic inter-city mobility laws,and they have significant applications for regional policies on human mobility.

Autophagy and cancer treatment: four functional forms of autophagy and their therapeutic applications

Cancer is the leading cause of death worldwide. Drugs play a pivotal role in cancer treatment, but the complex biological processes of cancer cells seriously limit the efficacy of various anticancer drugs. Autophagy, a self-degradative system that maintains cellular homeostasis, universally operates under normal and stress conditions in cancer cells. The roles of autophagy in cancer treatment are still controversial because both stimulation and inhibition of autophagy have been reported to enhance the effects of anticancer drugs. Thus, the important question arises as to whether we should try to strengthen or suppress autophagy during cancer therapy. Currently, autophagy can be divided into four main forms according to its different functions during cancer treatment: cytoprotective(cell survival), cytotoxic(cell death), cytostatic(growth arrest), and nonprotective(no contribution to cell death or survival). In addition, various cell death modes, such as apoptosis, necrosis, ferroptosis, senescence, and mitotic catastrophe, all contribute to the anticancer effects of drugs. The interaction between autophagy and these cell death modes is complex and can lead to anticancer drugs having different or even completely opposite effects on treatment. Therefore, it is important to understand the underlying contexts in which autophagy inhibition or activation will be beneficial or detrimental.That is, appropriate therapeutic strategies should be adopted in light of the different functions of autophagy. This review provides an overview of recent insights into the evolving relationship between autophagy and cancer treatment.

Multiplex network reconstruction for the coupled spatial diffusion of infodemic and pandemic of COVID-19

The pandemic of COVID-19 witnessed a massive infodemic with the public being bombarded with vast quantities of information.The spreading of neutral and highly accurate reports can guide the public to self-protect and reduce the pandemic.Mis-and dis-information would intrigue panic and high exposure risk to epidemic.Although the infodemic has attracted attentions from the academia,it is still not known to what degree and in which direction the information flows contribute to the COVID-19 pandemic.To fill the gap,we apply network reconstruction techniques to rebuild the hidden multiplex network of information and COVID-19 spreading by which we aim at quantifying the interaction between the propagation of information and the spatial outbreak of COVID-19,and delineate between the positive and negative impact of information on the pandemic.By differentiating the types of media that participated in the information process,we find that in the early stage of COVID-19 pandemic,infodemic does play a critical role to amplify the risk of virus outbreak in China and the risk is even larger for those highly developed regions.Compared to the old-fashion media,the new mobile platforms impose a greater risk to reinforce the positive feedback between infodemic and COVID-19 pandemic.

Modelling impacts of high-speed rail on urban interaction with social media in China’s mainland

High-Speed Rail(HSR)has increasingly become an important mode of inter-city transportation between large cities.Inter-city interaction facilitated by HSR tends to play a more prominent role in promoting urban and regional economic integration and development.Quantifying the impact of HSR’s interaction on cities and people is therefore crucial for long-term urban and regional development planning and policy making.We develop an evaluation framework using toponym information from social media as a proxy to estimate the dynamics of such impact.This paper adopts two types of spatial information:toponyms from social media posts,and the geographical location information embedded in social media posts.The framework highlights the asymmetric nature of social interaction among cities,and proposes a series of metrics to quantify such impact from multiple perspectives-including interaction strength,spatial decay,and channel effect.The results show that HSRs not only greatly expand the uneven distribution of inter-city connections,but also significantly reshape the interactions that occur along HSR routes through the channel effect.

Development and implementation of a dynamic and 4D GIS based on semantic location model

In the big data era,robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making.Current cartographic and geographic information systems have limited capabilities for solving these problems.This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources.In this system,a new Semantic Location Model(SemLM)is established to present the semantic concepts and location feature and demonstrate how locations are interrelated.In the SemLM,various types of location descriptors in different application scenarios can be analyzed and understood.Additionally,considering the challenges involved in data-intensive computation and visualization,this paper implements a Place-based Pan-Information System(P2S)as an innovative 4D system that dynamically associates and visualizes place-based information,using public security as the case study.

Qualitative spatial reasoning on topological relations by combining the semantic web and constraint satisfaction

Qualitative spatial reasoning on topological relations can extract hidden spatial knowledge from qualitatively described topological information,which is of significant importance for decisionmaking and query optimization in spatial analysis.Qualitative reasoning on spatial topological information based on semantic knowledge and reasoning rules is an efficient means of reducing both the known relations and the corresponding rules,which can result in enhanced reasoning performance.This paper proposes a qualitative reasoning method for spatial topological relations based on the semantic description of reasoning rules and constraint set.Combined with knowledge from the Semantic Web,the proposed method can easily extract potential spatial results consistent with both unique and non-unique rules.The Constraint-Satisfactionbased approach,describing constraint set with semantic expressions,is then used together with an improved path consistency algorithm to verify the consistency of the unique-rules-based and non-unique-rules-based reasoning results.The verification can eliminate certain reasoning results to ensure the reliability of the final results.Thus,the task of qualitative spatial reasoning on topological relations is completed.

Detecting events from the social media through exemplar-enhanced supervised learning

Understanding and detecting the intended meaning in social media is challenging because social media messages contain varieties of noise and chaos that are irrelevant to the themes of interests.For example,conventional supervised classification approaches would produce inconsistent solutions to detecting and clarifying whether any given Twitter message is really about a wildfire event.Consequently,a renovated workflow was designed and implemented.The workflow consists of four sequential procedures:(1)Apply the latent semantic analysis and cosine similarity calculation to examine the similarity between Twitter messages;(2)Apply Affinity Propagation to identify exemplars of Twitter messages;(3)Apply the cosine similarity calculation again to automatically match the exemplars to known training results,and(4)Apply accumulative exemplars to classify Twitter messages using a support vector machine approach.The overall correction ratio was over 90%when a series of ongoing and historical wildfire events were examined.

Automating land parcel classification for neighborhood-scale urban analysis

Homeowners’Associations(HOAs)dictate landscape structure and management through legally enforceable land covenants at the neighborhood scale in the USA.Determining the location and spatial extent of HOAs is critical for examining its influence.However,such analysis is confounded by the lack of spatial data at the appropriate unit for such analysis.The purpose of this paper is to develop and realize an open source implementation to automate land parcel classification,which is an initial step towards the goal of determining the impact of HOAs on urban land management.Using Maricopa County,Arizona as a testbed,we found that parcel merging processes reduce the number of subdivisions from 26,042 to 17,269,such that boundaries better align with neighborhood units to which rule sets like land covenants apply.Moreover,after an initial training period,this process was completed in just over 7 hours.This research is an important first step in enabling a number of analysis including determining the location and spatial extent of HOAs regionally and,eventually,nationally and determining proposed links between HOAs and land management outcomes.

The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the U.S. during the COVID-19 pandemic

This study reveals the human mobility from various sources and the luxury nature of social distancing in the U.S during the COVID-19 pandemic by highlighting the disparities in mobility dynamics from lower-income and upper-income counties.We collect,process,and compute mobility data from four different sources.We further design a Responsive Index(RI)based on the time series of mobility change percentages to quantify the general degree of mobility-based responsiveness to COVID-19 at the U.S.county level.We find statistically significant positive correlations in the RI between either two data sources,revealing their general similarity,albeit with varying Pearson’s r coefficients.Despite the similarity,however,mobility from each source presents unique and even contrasting characteristics,in part demonstrating the multifaceted nature of human mobility.The results suggest that counties with higher income tend to react more aggressively in terms of reducing more mobility in response to the COVID-19 pandemic.Most states present a positive difference in RI between their upper-income and lower-income counties,where diverging patterns in time series of mobility changes percentages can be found.The findings shed light on not only the characteristics of multi-source mobility data but also the mobility patterns in tandem with the economic disparity.

Scalable near-repeat and event chain calculations over heterogeneous computer architecture and systems

As a case of space-time interaction,near-repeat calculation indicates that when an event takes place at a certain location,its immediate geographical surroundings would face an increased risk of experiencing subsequent events within a fairly short period of time.This paper presents an exploratory study that extends the investigation of the near-repeat phenomena to a series of space-time interaction,namely event chain calculation.Existing near-repeat tools can only deal with a limited amount of data due to computation constraints,let alone the event chain analysis.By deploying the modern accelerator technology and hybrid computer systems,this study demonstrates that large-scale near-repeat calculation or event chain analysis can be partially resolved through high-performance computing solutions to advance such a challenging statistical problem in both spatial analysis and crime geography.