Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Environmental Analysis Using Integrated GIS and Spatial Configurations in Israel

The scope for environmental analysis constitutes a critical factor in recent times, yet demanding importance due to the concerns of environmental sustainability. The study aims at analysing the prospects of implementing an integrated GIS and spatial configuration for environment analysis in Israel. The study adopts an empirical study design to consider the multi-dimensional utilisation of an integrated GIS and spatial configuration for environment analysis. The study considers the materials and methods of the GIS system modelling as well, consisting of satellite imagery, GPS-based location identification, Esri ArcGIS, CyberGIS, and BIM integration to present a comprehensive system for the environmental analysis of Israel. The results of the study indicate that the threats of natural disasters and climate change can be identified based on the synergy of spatial data within an integrated GIS modelling. In many cases, it is also used in collaboration with a BIM to ensure that planning and decision-making processes are sustainable, economically beneficial and environmentally considered. Thus, it is concluded that environmental analysis through the projection of visually represented satellite imagery within an integrated GIS with spatial configurations in Israel can minimise the conflicts between the infrastructural designs, human activities, and environmental sustainability.

A comprehensive framework for exploratory spatial data analysis:Moran location and variance scatterplots

A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).

THE STRATEGIES OF INTEGRATING SPATI AL DATA ANALYSIS AND GIS

The gap between SDA(Spatial Data Analysis)and GIS(Geographical Information Systems )existed for a long time.Presently this problem still remains in spite of a lot of theore tical and practical studies which tr y to find the solu-tion for it.The research background and current situation about how to in tegrate SDA and GIS are introduced at first.The main idea of this article is to make su re what is the best scheme to bridge th e gap between SDA and GIS and how to design it.There are a lot of factors to influ ence the standards to assess such a sc heme,for instance,the attitude of users and GIS developers,the framework and related functions of current available GI S software in the market and so on.But the two most important ones of them are effic iency and flexibility of the scheme i tself.Efficiency can be measured by the conve-nient extent and temporal length when it is used for carrying out SDA.Flex ibility means users can define their own SDA methods.The best integration schem e should satisfy the two standards at the same time.A group of functions,which can be combined to implement any SDA meth od,are defined in order to design such an integration scheme.The functio ns are divided into five classes according to their properties.

Object-Based Analysis of Multispectral RS Data and GIS for Detection of Climate Change Impact on the Karakoram Range Northern Pakistan

Changing climate has a great impact on northern area of Pakistan＇s environment and is more prone to environmental changes impacts than rest of the country due to its high elevation. However, melting glaciers effect not only the local environment but also the whole country with frequent and heavy floods. Remote sensing （RS） from Satellites and Airplanes used in Geographical Information Systems （GIS） are technologies that can aid in understanding the on-going environmental processes. Furthermore, help researchers to observe, understand, forecast and suggest response to changes that occur. It can be natural disasters or man-made disasters and human induced factors. Still analysis accuracy issues play a vital role for the formulation of any strategy. To achieve better results, object based analysis methods have been tested. Various algorithms are developed by the analysts to calculate the magnitude of land cover changes. However, they must be evaluated for each environment that is under observation as mountainous areas. Here were object-based methods evaluated in comparison with pixel based. Landslides, soil moisture, soil permeability, snow cover and vegetation cover can be effectively monitored by those methods.

Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin,China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment.Industrialization and urbanization promote social-economic development,but they also have generated a series of environmental and ecological issues in this basin.Previous researches have evaluated urban resilience at the national,regional,urban agglomeration,city,and prefecture levels,but not at the watershed level.To address this research gap and elevate the Yellow River Basin’s urban resilience level,we constructed an urban resilience evaluation index system from five dimensions:industrial resilience,social resilience,environmental resilience,technological resilience,and organizational resilience.The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin.The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010,2015,and 2020.Furthermore,the grey correlation analysis method was utilized to explore the influencing factors of these differences.The results of this study are as follows:(1)the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015,and significant spatial distribution differences were observed,with a higher resilience level in the eastern region and a low-medium resilience level in the western region;(2)the differences in urban resilience were noticeable,with industrial resilience and social resilience being relatively highly developed,whereas organizational resilience and environmental resilience were relatively weak;and(3)the correlation ranking of resilience influencing factors was as follows:science and technology level>administrative power>openness>market forces.This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

Spatial Pattern and Regional Types of Rural Settlements in Xuzhou City,Jiangsu Province,China

This paper principally focuses on the morphological differences,spatial pattern and regional types of rural settlements in Xuzhou City of Jiangsu Province in China.Using satellite images of Xuzhou City taken in 2007 and 2008 and models of exploratory spatial data analysis(ESDA) and spatial metrics,the paper conducts a quantitative analysis of the morphological pattern of rural settlements,and finds significant characteristics.First,rural settlements in Xuzhou City are significantly agglomerated in terms of their spatial distribution;meanwhile,there is significant variation in the geographical density distribution.Second,the scale of rural settlements in Xuzhou City is larger than the average in Jiangsu Province,and the histogram of the scale data is more even and more like a gamma distribution.There are a significant high-value cluster in the scale distribution,and local negative correlation between the scale and density distribution of rural settlements in Xuzhou City.Third,the morphology of rural settlements in Xuzhou City shows relative regularity with good connection and integrity,but the spatial variation of the morphology is anisotropic.Finally,according to the characteristics of density,scale,and form of rural settlements,the rural settlements of Xuzhou City are divided into three types:A high-density and point-scattered type,a low-density and cluster-like type and a mass-like and sparse type.The research findings could be used as the scientific foundation for rural planning and community rebuilding,particularly in less-developed areas.

Detection of spatial hot spots and variation for the neon flying squid Ommastrephes bartramii resources in the northwest Pacific Ocean

With the increasing effects of global climate change and fishing activities,the spatial distribution of the neon flying squid(Ommastrephes bartramii) is changing in the traditional fishing ground of 150°-160°E and 38°-45°N in the northwest Pacific Ocean.This research aims to identify the spatial hot and cold spots(i.e.spatial clusters) of O.bartramii to reveal its spatial structure using commercial fishery data from2007 to 2010 collected by Chinese mainland squid-j igging fleets.A relatively strongly-clustered distribution for O.bartramii was observed using an exploratory spatial data analysis(ESDA) method.The results show two hot spots and one cold spot in 2007 while only one hot and one cold spots were identified each year from2008 to 2010.The hot and cold spots in 2007 occupied 8.2%and 5.6%of the study area,respectively;these percentages for hot and cold spot areas were 5.8%and 3.1%in 2008,10.2%and 2.9%in 2009,and 16.4%and 11.9%in 2010,respectively.Nearly half(>45%) of the squid from 2007 to 2009 reported by Chinese fleets were caught in hot spot areas while this percentage reached its peak at 68.8%in 2010,indicating that the hot spot areas are central fishing grounds.A further change analysis shows the area centered at156°E/43.5°N was persistent as a hot spot over the whole period from 2007 to 2010.Furthermore,the hot spots were mainly identified in areas with sea surface temperature(SST) in the range of 15-20℃ around warm Kuroshio Currents as well as with the chlorophyll-a(chl-a) concentration above 0.3 mg/m^3.The outcome of this research improves our understanding of spatiotemporal hotspots and its variation for O.bartramii and is useful for sustainable exploitation,assessment,and management of this squid.

Spatio-temporal evolution and factor explanatory power analysis of urban resilience in the Yangtze River Economic Belt

Urban resilience assesses a city’s ability to withstand unknown risks.Scholars are not comprehensive in assessing urban resilience,and they lack consideration of population resilience.This study investigated 110 prefecturelevel cities in the Yangtze River Economic Belt(YREB)as study areas.We calculated the YREB’s level of urban resilience based on the aspects of“economy-society-population-ecology-infrastructure”,which ensured that the comprehensive evaluation of urban resilience is complete and sufficient.The spatio-temporal evolution of urban resilience was analyzed using exploratory spatial data.Geodetectors were used to investigate the impact of several indicators,focusing on economic,social,population,ecological,and infrastructure factors,on urban resilience.The results showed that the urban resilience of the YREB has maintained a slow upward trend from 2005 to 2018,and the average urban resilience of the YREB has risen from 0.2442 to 0.2560.The resilience gap between cities in the study region increased initially and then decreased.The dominant factor in the spatial differentiation of urban resilience was the economic factors,followed by the population factors.Urban resilience has been clarified and an evaluation index system is constructed,which can provide an effective reference for the evaluation of urban resilience among countries around the world.Based on this,factors that optimize urban resilience are configured,and the regional and national sustainable development can be promoted.

Evaluating Water Withdrawals for Regional Water Management Under a Data-driven Framework

With an increase in population and economic development,water withdrawals are close to or even exceed the amount of water available in many regions of the world.Modelling water withdrawals could help water planners improve the efficiency of water use,water resources allocation,and management in order to alleviate water crises.However,minimal information has been obtained on how water withdrawals have changed over space and time,especially on a regional or local scale.This research proposes a data-driven framework to help estimate county-level distribution of water withdrawals.Using this framework,spatial statistical methods are used to estimate water withdrawals for agricultural,industrial,and domestic purposes in the Huaihe River watershed in China for the period 1978–2018.Total water withdrawals were found to have more than doubled,from 292.55×10^(8)m^(3) in 1978 to 642.93×10^(8)m^(3) in 2009,and decreased to 602.63×10^(8)m^(3) in 2018.Agricultural water increased from 208.17×10^(8)m^(3) in 1978 to 435.80×10^(8)m^(3) in 2009 and decreased to 360.84×10^(8)m^(3) in 2018.Industrial and domestic water usage constantly increased throughout the 1978–2018 period.In 1978,industrial and domestic demands were 20.35×10^(8)m^(3) and 60.04×10^(8)m^(3),respectively,and up until 2018,the figures were 105.58×10^(8)m^(3) and 136.20×10^(8)m^(3).From a spatial distribution perspective,Moran’s I statistical results show that the total water withdrawal has significant spatial autocorrelation during 1978–2018.The overall trend was a gradual increase in 1978–2010 with withdrawal beginning to decline in 2010–2018.The results of Getis-Ord G_(i)^(*)statistical calculations showed spatially contiguous clusters of total water withdrawal in the Huaihe River watershed during1978–2010,and the spatial agglomeration weakened from 2010 to 2018.This study provides a data-driven framework for assessing water withdrawals to enable a deeper understanding of competing water use among economic sectors as well as water withdrawal modelled with proper data resource and method.

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

Changing Rural Development Inequality in Jilin Province, Northeast China

Rural development inequality is an important practical issue during the course of full establishment of a ′moderately well-off society′ in modern China,and the objective understanding and evaluation of the status and regional inequality of rural development can provide scientific basis for ′building a new countryside′ and coordination development of rural-urban regions.Based on the county-level data of 2000,2005 and 2009,this paper examines the rural development inequality of Jilin Province in Northeast China by establishing a rural development index.The spatio-temporal dynamic patterns and domain factors are discussed by using the method of exploratory spatial data analysis and multi-regression model.The results are shown as follows.Firstly,most of the counties were in lower development level,which accounted for 58.3%,62.5% and 66.7% of the total counties in 2000,2005 and 2009,respectively.The characteristics of spatial inequality were very obvious at county level.For example,rural development level of Changchun Proper and the proper of seven prefecture-level cities were much higher than that of the surrounding regions.The counties in the eastern and northern Jilin Province were the lowest regions of rural development level,while the middle counties were the rapid growth areas in rural economy.Secondly,Moran′s I of rural development index(RDI) was 0.01,–0.16 and –0.06 in 2000,2005 and 2009,respectively,which indicated that spatial agglomeration of RDI was not obvious in Jilin Province,and took on the characteristic of random distribution.The counties of both the units and its adjacent units have higher development level(HH) were transferred from the western areas to the eastern areas,while the countries of both the units and its adjacent units have lower development level(LL) were diffused from the eastern to middle and western Jilin Province.Finally,the result of multi-regression analysis showed that the improvement of agricultural production condition,development of agricultural economics and the adjustment of industrial structure were the domain factors affecting rural development inequality of Jilin Province in the later ten years.

Spatio-temporal Dynamic of Quality of Life of Residents, Northeast China

Quality of life(QOL) is a hotspot issue that has attracted increasing attention from the Chinese Government and scholars, it is also a vital issue that should be addressed during the cause of ′establishing overall well-off society′. Northeast China is one of the most import old industrial bases in China, however, the industrial structure of heavy chemical industry and the development mode of ′production first, living last′ have leaded to series of social problems, which have also become a serious bottleneck to social stability and economic sustainable development. Through applying the methods of BP neural network, exploratory spatial data analysis(ESDA) and spatial regression model, this paper examines the space-time dynamics of QOL of the residents in Northeast China. We first investigate the indexes of QOL of the residents and then use ESDA methods to visualize its space-time relationship. We have found a spatial agglomeration of QOL of the residents in middle-southern Liaoning Province, central Jilin Province and Harbin-Qiqihar-Daqing area of Heilongjiang Province. Two third of the counties are low-low spatial correlation, and the correlative type of about 60% of the prefecture level areas keeps stable, indicating QOL of the residents in Northeast China shows a certain character of path dependence or spatial locked. We have also found that economic strength and development levels of service industry have positive and obvious effect on QOL of the residents, while the effect of such indexes as the social service level and the proportion of the tertiary industries are less.

Sparse representation-based correlation analysis of non-stationary spatiotemporal big data

As the basic data of digital city and smart city research,Spatiotemporal series data contain rich geographic information.Alongside the accumulation of spatial time-series data,we are also encountering new challenges related to analyzing and mining the correlations among the data.Because the traditional methods of analysis also have their own suitable condition restrictions for the new features,we propose a new analytical framework based on sparse representation to describe the time,space,and spatial-time correlation.First,before analyzing the correlation,we discuss sparse representation based on the K-singular value decomposition(K-SVD)algorithm to ensure that the sparse coefficients are in the same sparse domain.We then present new computing methods to calculate the time,spatial,and spatial-time correlation coefficients in the sparse domain;we then discuss the functions’properties.Finally,we discuss change regulations for the gross domestic product(GDP),population,and Normalized Difference Vegetation Index(NDVI)spatial time-series data in China’s Jing-Jin-Ji region to confirm the effectiveness and adaptability of the new methods.

The measure and characteristics of spatial-temporal evolution of China＇s science and technology resource allocation efficiency

According to the connotation and structure of science and technology resources and some relevant data of more than 286 cities at prefecture level and above during 2001-2010, using modified method--Data Envelopment Analysis （DEA）, science and tech- nology （S＆T） resource allocation efficiency of different cities in different periods has been figured out, which, uncovers the distributional difference and change law of S＆T resource allocation efficiency from the time-space dimension. Based on that, this paper has analyzed and discussed the spatial distribution pattern and evolution trend of S＆T resource allocation efficiency in different cities by virtue of the Exploratory Spatial Data Analysis （ESDA）. It turned out that： （1） the average of S＆T resource allocation efficiency in cities at prefecture level and above has always stayed at low levels, moreover, with repeated fluctuations between high and low, which shows a decreasing trend year by year. Besides, the gap between the East and the West is widening. （2） The asymmetrical distribution of S＆T resource allocation effi- ciency presents a spatial pattern of successively decreasing from Eastern China, Central China to Western China. The cities whose S＆T resource allocation efficiency are at higher level and high level take on a cluster distribution, which fits well with the 23 forming urban agglomerations in China. （3） The coupling degree between S＆T resource allocation efficiency and economic environment assumes a certain positive correlation, but not completely the same. The differentiation of S＆T resource allocation efficiency is common in regional devel- opment, whose existence and evolution are directly or indirectly influenced by and regarded as the reflection of many elements, such as geographical location, the natural endowment and environment of S＆T resources and so on. （4） In the perspective of the evolution of spatial structure, S＆T resource allocation efficiency of the cities at prefecture level and above shows a notable spatial autocorrelation, which in every period presents a positive correlation. The spatial distribution of S＆T resource allocation efficiency in neighboring cities seems to be similar in group, which tends to escalate stepwise. Meanwhile, the whole differentiation of geographical space has a diminishing tendency. （5） Viewed from LISA agglomeration map of S＆T resource allocation efficiency in different periods, four agglomeration types have changed differently in spatial location and the range of spatial agglomeration. And the conti- nuity of S＆T resource allocation efficiency in geographical space is gradually increasing.

ndustrial eco-efficiency and its spatial-temporal differentiation in China

The aim of this paper is to study the spatialtemporal differentiation of industrial eco-efficiency in China. Using methods based on the data envelopment analysis （DEA） model and exploratory spatial data analysis （ESDA） and data from 1985, 1995, 2005, and 2008 of 30 provinces in China, the spatial-temporal pattern changes in industrial eco-efficiency are discussed. The results show that： first, the patterns of industrial eco-efficiency are dominated by clustering of relatively low efficiency provinces; second, spatial relationships between the industrial eco-efficiencies of different provinces changed slightly throughout the period and the provinces persistently exhibit spatial concentration of relatively low industrial eco-efficiency; finally, there is an obvious trend in the polarization of industrial eco-efficiency, i.e., the higher level spatial units are concentrated in eastern China, and the lower level spatial units are mainly in western and central China. （ESDA）

Analyzing space-time evolution of rural transition in a rapidly urbanizing region:A case study of Suzhou,China

Influenced by globalization,rural transition in developed Western countries has experienced processes of productivism,post-productivism,and multifunctional development.By contrast,rural transition in most developing countries has been accompanied by rapid urbanization,which has become a core topic in geography research.As the world’s largest developing country,China has undergone profound development since the reform and opening-up.Moreover,rural spaces in some eastern coastal areas have entered the stage of reconstruction after decades of industrialization and urbanization.This paper takes Suzhou as the case area and measures the process of rural transition from 1990 to 2015 by constructing an index system.It then analyzes the characteristics of space-time evolution using exploratory spatial data analysis(ESDA)methods to reveal the influence of economic and social development on rural transition.The results show that rural transition,which generally entails the weakening of rurality and enhancing of urbanity on a macro scale,tends to be heterogeneous across different regions on a micro scale.This paper argues that multifunctionality will be the main future trend of rural transition in rapidly urbanizing areas.The experience in Suzhou could provide an example for establishing policies on sustainable development in rural spaces and achieving urban-rural co-governance.