Rice cultivation changes and its relationships with geographical factors in Heilongjiang Province,China

Rice planting patterns have changed dramatically over the past several decades in northeast China （NEC） due to the combined influence of global change and agricultural policy. Except for its great implications for environmental protection and climate change adaption, the spatio-temporal changes of rice cultivation in NEC are not clear. In this study, we conducted spatio-temporal analyses of NEC＇s major rice production region, Heilongjiang Province, by using satellite-derived rice cultivation maps. We found that the total cultivated area of rice in Heilongjiang Province increased largely from 1993 to 2011 and it expanded spatially to the northern and eastern part of the Sanjiang Plain. The results also showed that rice cultivation areas experienced a larger increase in the region managed by the Reclamation Management Bureau （RMB） than that managed by the local provincial government. Rice cultivation changes were closely related with those geographic factors over the investigated periods, represented by the geomorphic （slope）, climatic （accumulated temperature）, and hydrological （watershed） variables. These findings provide clear evidence that crop cultivation in NEC has been modified to better cope with the global change.

Constructing a raster-based spatio-temporal hierarchical data model for marine fisheries application

Marine information has been increasing quickly. The traditional database technologies have disadvantages in manipulating large amounts of marine information which relates to the position in 3-D with the time. Recently, greater emphasis has been placed on GIS （geographical information system）to deal with the marine information. The GIS has shown great success for terrestrial applications in the last decades, but its use in marine fields has been far more restricted. One of the main reasons is that most of the GIS systems or their data models are designed for land applications. They cannot do well with the nature of the marine environment and for the marine information. And this becomes a fundamental challenge to the traditional GIS and its data structure. This work designed a data model, the raster-based spatio-temporal hierarchical data model （RSHDM）, for the marine information system, or for the knowledge discovery fi＇om spatio-temporal data, which bases itself on the nature of the marine data and overcomes the shortages of the current spatio-temporal models when they are used in the field. As an experiment, the marine fishery data warehouse （FDW） for marine fishery management was set up, which was based on the RSHDM. The experiment proved that the RSHDM can do well with the data and can extract easily the aggregations that the management needs at different levels.

建成环境对地铁节假日客流影响的时空异质性研究

地铁客流的时空特征与线网空间结构有较强相关性,因此明确建成环境对客流影响的时空异质性十分重要,时空异质性是指在不同时空状态下存在差异。文章以杭州地铁为实例,结合城市多源数据的挖掘与应用、构建建成环境因素集,建立了基于高德点阵分布法的4种车站聚类模型,结合最优聚类结果、基于时空地理加权回归模型细化时空维度,得出了6种建成环境因素对五一节假日、十一节假日客流在每日和每类车站的影响程度结论及原因分析。

Spatio-temporal Pattern of Surface Albedo in Beijing and Its Driving Factors based on Geographical Detectors

Surface albedo directly affects the radiation balance and surface heat budget,and is a crucial variable in local and global climate research.In this study,the spatial and temporal distribution of the surface albedo is analysed for Beijing in 2015,and the corresponding individual and interactive driving forces of different explanatory factors are quantitatively assessed based on geographical detectors.The results show that surface albedo is high in the southeast and low in the northwest of Beijing,with the greatest change occurring in winter and the smallest change occurring in spring.The minimum and maximum annual surface albedo values occurred in autumn and winter,respectively,and showed significant spatial and temporal heterogeneity.LULC,NDVI,elevation,slope,temperature,and precipitation each had a significant influence on the spatial pattern of albedo,yielding explanatory power values of 0.537,0.625,0.512,0.531,0.515 and 0.190,respectively.Some explanatory factors have significant differences in influencing the spatial distribution of albedo,and there is significant interaction between them which shows the bivariate enhancement result.Among them,the interaction between LULC and NDVI was the strongest,with a q-statistic of 0.710,while the interaction between temperature and precipitation was the weakest,with a q-statistic of 0.531.The results of this study provide a scientific basis for understanding the spatial and temporal distribution characteristics of surface albedo in Beijing and the physical processes of energy modules in regional climate and land surface models.

Infuence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year’s longitudinal surveillance data:a spatio-temporal modelling study

Background Urbanization greatly afects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases.Schistosomiasis,a common parasitic disease transmitted by the snail Oncomelania hupensis,is mainly found in areas with population aggregations along rivers and lakes where snails live.Previous studies have suggested that factors related to urbanization may infuence the infection risk of schistosomiasis,but this association remains unclear.This study aimed to analyse the efect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.Methods County-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected.The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite(NPP-VIIRS).The geographically and temporally weighted regression model(GTWR)was used to quantify the infuence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled.The regression coefcient of urbanization was tested for signifcance(α=0.05),and the infuence of urbanization on schistosomiasis infection risk was analysed over time and across space based on signifcant regression coefcients.Variables studied included climate,soil,vegetation,hydrology and topography.Results The mean regression coefcient for urbanization(0.167)is second only to the leached soil area(0.300),which shows that the urbanization is the most important infuence factors for schistosomiasis infection risk besides leached soil area.The other important variables are distance to the nearest water source(0.165),mean minimum temperature(0.130),broadleaf forest area(0.105),amount of precipitation(0.073),surface temperature(0.066),soil bulk density(0.037)and grassland area(0.031).The infuence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year.During the study period,the signifcant coefcient of urbanization level increased from−0.205 to−0.131.Conclusions The infuence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous.The urbanization does reduce the risk of schistosomiasis infection to some extend,but the strength of this infuence decreases with increasing urbanization.Additionally,the efect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors.This study provides scientifc basis for understanding the infuence of urbanization on schistosomiasis,and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Spatial/temporal indexing and information visualization genre for environmental digital libraries

Protecting and preserving our environmental systems require the ability to understand the spatio-temporal distri- bution of soils, parent material, topography, and land cover as well as the effects of human activities on ecosystems. Space-time modelling of ecosystems in an environmental digital library is essential for visualizing past, present, and future impacts of changes occurring within such landscapes (e.g., shift in land use practices). In this paper, we describe three novel features, spa- tio-temporal indexing, visualization, and geostatistical genre, for the environmental digital library, Environmental Visualization and Geographic Enterprise System (ENVISAGE), currently in progress at the University of Florida.

Modeling the spread of spatio-temporal phenomena through the incorporation of ANFIS and genetically controlled cellular automata: a case study on forest fire

Virtual representation and simulation of spatio-temporal phenomena is a promising goal for the production of an advanced digital earth.Spread modeling,which is one of the most helpful analyses in the geographic information system(GIS),plays a prominent role in meeting this objective.This study proposes a new model that considers both aspects of static and dynamic behaviors of spreadable spatio-temporal in cellular automata(CA)modeling.Therefore,artificial intelligence tools such as adaptive neuro-fuzzy inference system(ANFIS)and genetic algorithm(GA)were used in accordance with the objectives of knowledge discovery and optimization.Significant conditions in updating states are considered so traditional CA transition rules can be accompanied with the impact of fuzzy discovered knowledge and the solution of spread optimization.We focused on the estimation of forest fire growth as an important case study for decision makers.A two-dimensional cellular representation of the combustion of heterogeneous fuel types and density on non-flat terrain were successfully linked with dynamic wind and slope impact.The validation of the simulation on experimental data indicated a relatively realistic head-fire shape.Further investigations showed that the results obtained using the dynamic controlling with GA in the absence of static modeling with ANFIS were unacceptable.

A new model to extract environmental pattern for fishing ground

The relationship between the forming of fishing ground and the oceanic spatio-temporal field is nonlinear. In order to analyse the relationship between variables with spatio-temporal structures, a spatio-temporal pattern extracting model is provided to find the reason for the forming of fishing ground. In the model, the spatio-temporal structure of the oceanic factors is expressed as neighbour. The elements in the neighbour are filled in the table of decision-making system. After a recursion processing the spatio-temporal rule will be extracted with the spatio-temporal structure. And a experiment is given to show how to find the temperature pattern for the forming of the fishing grounds in Dasha area. It shows that the model is active and it should be the research front for the marine fishery.

Processes and events in the center: a taxi trajectory-based approach to detecting traffic congestion and analyzing its causes

A novel approach is introduced for the detection of the location and direction of traffic congestion using GPS data from taxis.This approach uses a dynamic model that conceptualizes events,processes,and states.The states are the locations of the taxis,the processes are the motion of taxis,and the events are congestion.The model is implemented as a graph database,which represents the relationships between states,events,processes,and things(such as points of interest and road grid).Algorithms for constructing and updating the relationships and taxi behaviors dynamic retrieval method in Neo4j are presented and are used to demonstrate the capabilities in dynamic expression and reasoning.An implementation of Shanghai in 2015finally demonstrated the ability of congestion direction detection and the cause searching of traffic congestion.

Processes and events in the centre:a dynamic data model for representing spatial change

Traditional geographic information system models for map representation use superposition of layers to model physical reality,neglecting the integrity of the environment and limiting the ability to express interactions between features in complex phenomenon.This results in limitations regarding dynamic simulation and geographic causality reasoning.In this paper,we extend the framework of the geographic scene by formalizing the relationship between geographic processes and events to construct a dynamic data model:the process-event-centred dynamic data model.The key element of this data model is relationships between processes,events,and states of the natural or man-made phenomenon of interest.The identified relationships can be translated into a network of hierarchical,developmental,and causal graphs and realized in the Neo4j graph database.The implementation in the graph database supports spatio-temporal reasoning in geographic scenes and achieves an organizational framework for simulating spatio-temporal dynamics and complex calculations.The example of a 2019 mega-typhoon process is used to demonstrate the introduced process-event-centred model and its implementation in the graph database.A series of queries to the graph database show the capabilities of the data model for spatial reasoning and dynamic modeling.