Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient metho...Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.展开更多
The outbreak of the 2019 novel coronavirus disease(COVID-19)has caused more than 100,000 people infected and thousands of deaths.Currently,the number of infections and deaths is still increasing rapidly.COVID-19 serio...The outbreak of the 2019 novel coronavirus disease(COVID-19)has caused more than 100,000 people infected and thousands of deaths.Currently,the number of infections and deaths is still increasing rapidly.COVID-19 seriously threatens human health,production,life,social functioning and international relations.In the fight against COVID-19,Geographic Information Systems(GIS)and big data technologies have played an important role in many aspects,including the rapid aggregation of multi-source big data,rapid visualization of epidemic information,spatial tracking of confirmed cases,prediction of regional transmission,spatial segmentation of the epidemic risk and prevention level,balancing and management of the supply and demand of material resources,and socialemotional guidance and panic elimination,which provided solid spatial information support for decision-making,measures formulation,and effectiveness assessment of COVID-19 prevention and control.GIS has developed and matured relatively quickly and has a complete technological route for data preparation,platform construction,model construction,and map production.However,for the struggle against the widespread epidemic,the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management.At the data level,in the era of big data,data no longer come mainly from the government but are gathered from more diverse enterprises.As a result,the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data,which requires governments,businesses,and academic institutions to jointly promote the formulation of relevant policies.At the technical level,spatial analysis methods for big data are in the ascendancy.Currently and for a long time in the future,the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition,which signifies ts that GIS should be used to reinforce the social operation parameterization of models and methods,especially when providing support for social management.展开更多
Epidemic prediction is a crucial foundation of disease control policy-making. Owing to the high population connectivity of current epidemics, it is essential to capture the spatial transmission of infectious diseases....Epidemic prediction is a crucial foundation of disease control policy-making. Owing to the high population connectivity of current epidemics, it is essential to capture the spatial transmission of infectious diseases. However, most models currently used in epidemic prediction are single-point models, and they can only capture the time-dynamic increase of cases in limited areas. In this study, we develop a two-dimension epidemic prediction model by introducing diffusion processes, which take spatial transmission epidemics into account. We utilize mathematical theorems to prove a well-posed solution of the model. In addition, we also consider various influencing factors that affect the spread of epidemics, and introduce multiple parameterization schemes. Results suggest that this two-dimension model provides more precise predict the spatial and temporal distribution of confirmed cases. The regional average prediction score of COVID-19 in July 2022 in Lanzhou is 76.5 % and COVID-19 from May 1st to May 31st, 2023 in China is 70.7 %,respectively. Our results offer a scientific foundation for further study on the prediction of spatial epidemics, which contributes to an in-depth understanding of epidemic dynamics and provides valuable reference for the formulation of public health strategies and policies.展开更多
文摘Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.
基金funded by the National Natural Science Foundation of China(41421001,42041001 and 41525004).
文摘The outbreak of the 2019 novel coronavirus disease(COVID-19)has caused more than 100,000 people infected and thousands of deaths.Currently,the number of infections and deaths is still increasing rapidly.COVID-19 seriously threatens human health,production,life,social functioning and international relations.In the fight against COVID-19,Geographic Information Systems(GIS)and big data technologies have played an important role in many aspects,including the rapid aggregation of multi-source big data,rapid visualization of epidemic information,spatial tracking of confirmed cases,prediction of regional transmission,spatial segmentation of the epidemic risk and prevention level,balancing and management of the supply and demand of material resources,and socialemotional guidance and panic elimination,which provided solid spatial information support for decision-making,measures formulation,and effectiveness assessment of COVID-19 prevention and control.GIS has developed and matured relatively quickly and has a complete technological route for data preparation,platform construction,model construction,and map production.However,for the struggle against the widespread epidemic,the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management.At the data level,in the era of big data,data no longer come mainly from the government but are gathered from more diverse enterprises.As a result,the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data,which requires governments,businesses,and academic institutions to jointly promote the formulation of relevant policies.At the technical level,spatial analysis methods for big data are in the ascendancy.Currently and for a long time in the future,the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition,which signifies ts that GIS should be used to reinforce the social operation parameterization of models and methods,especially when providing support for social management.
基金supported by the Major Project of Guangzhou Laboratory,Grant No.GZNL 2024A01004National Key Research and Development Program of China(2023YFC3503400)Gansu Province Intellectual Property Program(Oriented Organization)Project(22ZSCQD02).
文摘Epidemic prediction is a crucial foundation of disease control policy-making. Owing to the high population connectivity of current epidemics, it is essential to capture the spatial transmission of infectious diseases. However, most models currently used in epidemic prediction are single-point models, and they can only capture the time-dynamic increase of cases in limited areas. In this study, we develop a two-dimension epidemic prediction model by introducing diffusion processes, which take spatial transmission epidemics into account. We utilize mathematical theorems to prove a well-posed solution of the model. In addition, we also consider various influencing factors that affect the spread of epidemics, and introduce multiple parameterization schemes. Results suggest that this two-dimension model provides more precise predict the spatial and temporal distribution of confirmed cases. The regional average prediction score of COVID-19 in July 2022 in Lanzhou is 76.5 % and COVID-19 from May 1st to May 31st, 2023 in China is 70.7 %,respectively. Our results offer a scientific foundation for further study on the prediction of spatial epidemics, which contributes to an in-depth understanding of epidemic dynamics and provides valuable reference for the formulation of public health strategies and policies.
