Journal articles on the topic 'Dynamic spatio-temporal queries'

Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and a spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge, but also the past. In addition, ST-RCQL includes a variety of time operators and time constants; thus, queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.

Recently, the trend of developing digital twins for smart cities has driven a need for managing large-scale multidimensional, dynamic, and scene-oriented spatial data. Due to larger data scale and more complex data structure, queries over such data are more complicated and expensive than those on traditional spatial data, which poses challenges to the system efficiency and deployment costs. The existing spatial databases have limited support in both data types and operations. Therefore, a new-generation spatial database with excellent performance and effective deployment costs is needed. This paper presents Ganos, a cloud-native spatial database engine of PolarDB for PostgreSQL that is developed by Alibaba Cloud, to efficiently manage multidimensional, dynamic, and scene-oriented spatial data. Ganos models 3D space and spatio-temporal dynamics as first-class citizens. Also, it natively supports spatial/spatio-temporal data types such as 3DMesh, Trajectory, Raster, PointCloud, etc. Besides, it implements a novel extended-storage mechanism that utilizes cloud-native object storage to reduce storage costs and enable uniform operations on the data in different storages. To facilitate processing "big" queries, Ganos extends PolarDB and provides spatial-oriented multi-level parallelism under the architecture of decoupling compute from storage in cloud-native databases, which achieves elasticity and excellent query performance. We demonstrate Ganos in real-life case studies. The performance of Ganos is evaluated using real datasets, and promising results are obtained. Finally, based on the extensive deployment and application of Ganos, the lessons learned from our customers and the expectations of modern cloud applications for new spatial database features are discussed.

Accurate, speedy and interoperable information exchange among the stakeholders achieve effective rescue and relief operations in an emergency. The current research work aims at location-based real time or near real time disaster data gathering and accumulation. The dynamic disaster data is integrated with the static geospatial data to facilitate spatial analytics and disseminate the integrated data through OGC web services to various stakeholders for further processing by different expert domain applications. The research work also facilitates spatio-temporal querying system through Geo-query, and OLAP operations on integrated disaster data with geospatial visualization. The design and implementation of the work is achieved through a mobile application integrated with a GIS based web portal by a centralized remote server. The entire architecture has been tested by implementing in an emergency situation and facilitated by an effective interoperable information exchange and spatio-temporal queries.

Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents (1) a state of the art review into visual analytics of geospatial, spatio-temporal streaming data, and (2) proposes a framework based on the identified gaps from the review. The framework consists of (1) the data model that characterizes the sensor observation data, (2) the user model, which addresses the user queries and manages domain knowledge, (3) the design model, which handles the patterns that can be uncovered from the data and corresponding visualizations, and (4) the visualization model, which handles the rendering of the data. The conclusion from the visualization model is that streaming sensor observations require tools that can handle multivariate, multiscale, and time series displays. The design model reveals that the most useful patterns are those that show relationships, anomalies, and aggregations of the data. The user model highlights the need for handling missing data, dealing with high frequency changes, as well as the ability to review retrospective changes.

Technological evolution in the remote sensing domain has allowed the acquisition of large archives of satellite image time series (SITS) for Earth Observation. In this context, the need to interpret Earth Observation image time series is continuously increasing and the extraction of information from these archives has become difficult without adequate tools. In this paper, we propose a fast and effective two-step technique for the retrieval of spatio-temporal patterns that are similar to a given query. The method is based on a query-by-example procedure whose inputs are evolution patterns provided by the end-user and outputs are other similar spatio-temporal patterns. The comparison between the temporal sequences and the queries is performed using the Dynamic Time Warping alignment method, whereas the separation between similar and non-similar patterns is determined via Expectation-Maximization. The experiments, which are assessed on both short and long SITS, prove the effectiveness of the proposed SITS retrieval method for different application scenarios. For the short SITS, we considered two application scenarios, namely the construction of two accumulation lakes and flooding caused by heavy rain. For the long SITS, we used a database formed of 88 Landsat images, and we showed that the proposed method is able to retrieve similar patterns of land cover and land use.

Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio-temporal data via V2X-based communication between vehicles and the infrastructure, which will become a central enabler for road safety of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. Our approach for semantic enhancement is in the context of ontology-mediated query answering (OQA) and features conjunctive queries over DL-LiteA ontologies that support window operators over streams and spatial relations between spatial objects. In this paper, we show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, traffic events detection, and advanced driving assistance systems. We define for the mentioned use cases requirements derived from necessary domain-specific features and report, based on them, on extensions of our query language and ontology model. The extensions include temporal relations, numeric predictions and trajectory predictions as well as optimization strategies such as caching. An experimental evaluation of queries that reflect the requirements has been conducted using the real-world traffic simulation tool PTV Vissim. It provides evidence for the feasibility/efficiency of our approach in the new scenarios.

Aquaculture site selection, like most site suitability analyses, requires the assembly and combination of multiple variables. Geographic information systems GIS and multi-criteria evaluation (MCE) based approaches are commonly used for aquaculture site selection and demonstrate the integration of various information sources relevant for siting aquaculture. These analyses, however, tend to be one-time and result in a fixed site suitability plan. Within a dynamic marine environment experiencing potential regime shifts, a siting support tool that integrates new and evolving spatio-temporal data has benefits. This paper presents a flexible Voronoi cell-based GIS model for marine aquaculture siting. Rather than a one-time specification of suitable locations, the approach uses similarity measures on the characteristics of Voronoi cells to find cells with similar characteristics. We calculate a weighted aquaculture site tenure value for Voronoi cells that have been or are occupied by aquaculture farm sites. High scoring cells suggest suitable sites and serve as targets for similarity queries. We apply the approach to a case study on the coast of Maine using an R Shiny application to demonstrate the use of the framework for finding sites with similar characteristics.

New spatio-textual indexing methods are needed to support efficient search and update of the massive amounts of spatially referenced text being generated. Location based services using geo-tagged documents provide valuable ranked recommendations about nearby restaurants, services, sales, emergency events, and visitor attractions. Consequently, top-k spatial keyword search queries (TkSKQ) have received a lot of attention from the research community. Several spatio-textual indexes have been proposed to efficiently support TkSKQ. Some of these indexes support updates based on live document streams, but the ranking schemes employed by them do not simultaneously incorporate temporal relevance, textual similarity and spatial proximity. Moreover, existing approaches have limited or no capability to exploit parallelism with document ingestion and query execution. We present a parallel spatio-textual index, Pastri, to address the aforementioned issues. Pastri can be updated incrementally over real-time spatio-textual document streams. To support temporally relevant ranking of continuously generated document streams, we propose a dynamic ranking scheme. Our approach retrieves the top-k documents that are most temporally relevant at the time of a query execution. We implemented Pastri and we integrate it within a system with a persistent document store and several thread pools to exploit parallelism at various levels. Experimental evaluation involving real-world datasets and synthetic datasets (that we created) demonstrates that our system is able to sustain high document update throughput. Furthermore, Pastri's TkSKQ search performance is one to two orders of magnitude faster than other spatio-textual indexes.

One viewpoint of current and future IT systems holds that there is an increase in the scale and velocity at which data are acquired and analysed from heterogeneous, dynamic sources. In the earth observation and geoinformatics domains, this process is driven by the increase in number and types of devices that report location and the proliferation of assorted sensors, from satellite constellations to oceanic buoy arrays. Much of these data will be encountered as self-contained messages on data streams - continuous, infinite flows of data. Spatial analytics over data streams concerns the search for spatial and spatio-temporal relationships within and amongst data “on the move”. In spatial databases, queries can assess a store of data to unpack spatial relationships; this is not the case on streams, where spatial relationships need to be established with the incomplete data available. Methods for spatially-based indexing, filtering, joining and transforming of streaming data need to be established and implemented in software components. This article describes the usage patterns and performance metrics of a number of well known FOSS4G Python software libraries within the data stream processing paradigm. In particular, we consider the RTree library for spatial indexing, the Shapely library for geometric processing and transformation and the PyProj library for projection and geodesic calculations over streams of geospatial data. We introduce a message oriented Python-based geospatial data streaming framework called Swordfish, which provides data stream processing primitives, functions, transports and a common data model for describing messages, based on the Open Geospatial Consortium Observations and Measurements (O&M) and Unidata Common Data Model (CDM) standards. We illustrate how the geospatial software components are integrated with the Swordfish framework. Furthermore, we describe the tight temporal constraints under which geospatial functionality can be invoked when processing high velocity, potentially infinite geospatial data streams. The article discusses the performance of these libraries under simulated streaming loads (size, complexity and volume of messages) and how they can be deployed and utilised with Swordfish under real load scenarios, illustrated by a set of Vessel Automatic Identification System (AIS) use cases. We conclude that the described software libraries are able to perform adequately under geospatial data stream processing scenarios - many real application use cases will be handled sufficiently by the software.

Abstract. The extent to which forest growth is limited by carbon (C) supply (source control) or by cambial activity (sink control) will condition the response of trees to global changes. However, the physiological processes responsible for the limitation of forest growth are still under debate. The aim of this study is to evaluate the key drivers of the annual carbon allocation to wood along large soil and climate regional gradients in five tree species representative of the main European forest biomes (Fagus sylvatica, Quercus petraea, Quercus ilex, Quercus robur and Picea abies). Combining field measurements and process-based simulations at 49 sites (931 site-years), we assessed the stand biomass growth dependences at both inter-site and inter-annual scales. Specifically, the relative influence of forest C balance (source control), direct environmental control (water and temperature controls of sink activity) and allocation adjustments related to age, past climate conditions, competition intensity and soil nutrient availability on growth were quantified. The inter-site variability in stand C allocation to wood was predominantly driven by an age-related decline. The direct control of temperature or water stress on sink activity (i.e. independently from their effects on C supply) exerted a strong influence on the annual stand woody growth in all the species considered, including deciduous temperate species. The lagged effect of the past environment conditions was a significant driver of the annual C allocation to wood. Carbon supply appeared to strongly limit growth only in deciduous temperate species. We provide an evaluation of the spatio-temporal dynamics of annual carbon allocation to wood in European forests. Our study supports the premise that European forest growth is under a complex control including both source and sink limitations. The relative influences of the different growth drivers strongly vary across years and spatial ecological gradients. We suggest a straightforward modelling framework to implement these combined forest growth limitations into terrestrial biosphere models.

Knowledge of the Holocene Indian Summer Monsoon (ISM) variability is important for understanding the spatio-temporal dynamics of the ISM precipitation. In this study, a Late Holocene pollen proxy record of the changes in the ISM intensity from a 1.8 m deep lacustrine sedimentary deposit in Jammu District (Jammu and Kashmir State) in India is presented. The results show that between ~3205 and 2485 cal yr BP, mixed broad-leaved/conifer forests occurred in the region under a warm and humid climate, probably indicating high monsoon precipitation. Subsequently, the conifers, such as Pinus sp., Cedrus sp., Abies sp., Picea sp. and Larix sp. increased comparatively and show dominance over the existing broad-leaved taxa between ~2485 and 1585 cal yr BP under a cool and dry climate with reduced monsoon precipitation. The climate further deteriorated (towards attaining aridity under reduced monsoon condition) during ~1585 to 865 cal yr BP, which coincides with the Dark Ages Cold Period (DACP: CE/AD 400–765; 1185–1550 cal yr BP). Since ~865 cal yr BP (CE/AD 1085 onwards) to Present, the broad-leaved taxa, such as Alnus sp., Betula sp., Ulmus sp., Carpinus sp., Corylus sp. and Quercus sp. started expanding and showed their dominance over the conifers, indicating a warm and humid climate in the region with increased monsoon precipitation. This phase partly corresponds with the Medieval Climatic Anomaly (MCA) between CE/AD 950 and 1300. Thus, the ISM rainfall intensity is linked with some of the global climatic trends in the present study.

Abstract. The extent to which wood growth is limited by carbon (C) supply (i.e. source control) or by cambial activity (i.e. sink control) will strongly determine the responses of trees to global changes. Nevertheless, the physiological processes that are responsible for limiting forest growth are still a matter of debate. The aim of this study was to evaluate the key determinants of the annual C allocation to wood along large soil and climate regional gradients over France. The study was conducted for five tree species representative of the main European forest biomes (Fagus sylvatica, Quercus petraea, Quercus ilex, Quercus robur and Picea abies). The drivers of stand biomass growth were assessed on both inter-site and inter-annual scales. Our data set comprised field measurements performed at 49 sites (931 site-years) that included biometric measurements and a variety of stand characteristics (e.g. soil water holding capacity, leaf area index). It was complemented with process-based simulations when possible explanatory variables could not be directly measured (e.g. annual and seasonal tree C balance, bioclimatic water stress indices). Specifically, the relative influences of tree C balance (source control), direct environmental control (water and temperature controls of sink activity) and allocation adjustments related to age, past climate conditions, competition intensity and soil nutrient availability on growth were quantified. The inter-site variability in the stand C allocation to wood was predominantly driven by age-related decline. The direct effects of temperature and water stress on sink activity (i.e. effects independent from their effects on the C supply) exerted a strong influence on the annual stand wood growth in all of the species considered, including deciduous temperate species. The lagged effect of the past environmental conditions (e.g. the previous year's water stress and low C uptake) significantly affected the annual C allocation to wood. The C supply appeared to strongly limit growth only in temperate deciduous species. We provide an evaluation of the spatio-temporal dynamics of the annual C allocation to wood in French forests. Our study supports the premise that the growth of European tree species is subject to complex control processes that include both source and sink limitations. The relative influences of the growth drivers strongly vary with time and across spatial ecological gradients. We suggest a straightforward modelling framework with which to implement these combined forest growth limitations into terrestrial biosphere models.

<p><strong>Abstract.</strong> Why do we teach cartography? The need for cartographic education:</p><p>In our day to day life, on an individual or societal level there is a continual need or even demand for geospatial information. On an individual level this need is expressed by questions like: Where am I?, How far away is my new doctor’s office?, Which route should I take to get to my destination based on current traffic patterns? Other questions may include: What is the spatial extent of my land parcel? What do I have permission to build on my parcel? On a societal level questions include: What cities suffer from high unemployment? What are the most efficient spots to build a new wind farm? Where is the optimal place to build a new road without fragmenting important species habitats? To offer answers to these questions, geographic information systems (GIS) including tools and instruments have been developed. The most important communication tool to foster decision making, as part of a GIS, is the map. Reality is too complex to comprehend with the naked eye. Therefore patterns are often missed, maps and other cartographic models are an interface between humans and the reality used to abstract, symbolized, a simplify view of the world. These maps then allow us to view spatial patterns and relationships between objects in the world. The world cannot do without maps. Why? Because they tell us about spatial issues on both local and global scale that influence our lives. How? Maps are the most effective and the most efficient tools to into and overview of geographical data which help us answer spatio-temporal questions and to provide new insight.</p><p> </p><p>What is ongoing in our world? Trends in our domain: yesterday, today and tomorrow:</p><p>Looking at the timeline of our domain, cartography, we could argue that after a long period where maps where seen as artifacts, maps are now considered to be interactive and dynamic (web) services, and in the near future we move to human centered cognitive map displays that are immersive and ubiquitous. Yesterday, the map could be considered an artifact, a static object, on paper or on a screen. The map stores the information and can no longer be changed. The user did not play a prominent role in map design. Today, with the internet, there has been a huge increase in data access and generation resulting in maps being produced and used especial to satisfy individual location-based queries such as ’Where am I right now’ and ‘How-do-I-get-there?’ questions. Societal questions are answered by maps available via automated services accessible via dedicated portals. Today maps are no longer artifacts, but provided as a digital map services. However, tomorrow the map will yet again be different. We are able to sense and monitor the world real time and ubiquitously, including human users’ spatial abilities, emotions, needs and requirements. With developments in interface design including more opportunities for 3d/4d/Virtual Reality/Augmented Reality Human-Computer-Interfaces are becoming even "closer" to our human processing system. Maps will increasingly become human-centered, highly interactive, dynamic and adjustable visual displays.</p><p> </p><p>Purpose: What are the cartographic consequences of these developments? Required cartographic competences:</p><p> The above developments have resulted in the expansion of what define the existing established cartographic method: making geospatial data and information accessible for users to foster discovery and insight into and overview of spatiotemporal data. Map design, including fundamentals such as projection, scale, generalization and symbolization, remain core to cartography. Yesterday, cartographic education was focused on how to optimally create fixed graphical representations at a defined scale constrained by the media, but with an eye for syntactical as well as graphical/aesthetical quality. Today knowledge and skills cartographers require have expanded, and they include an understanding of Spatial Data Infrastructures (SDI) that house Big Data and Data Science, Web Services, Programming, Style Definitions, Algorithms, Semantic web and Linked Data and Interactivity and other relevant technological skills. Increasingly, more attention has also been, and will have to be, paid to use and user (requirement) analysis and usability assessment. Users will simple not use cartographic services that are not enjoyable and do not help them meet their goals. We will continue to conduct usability evaluations in new sensing and map display environments. Based on technological advances and social uptake thereof, tomorrow will yet again ask for an adaption of the cartographic education and research dealing more and more with the "human" embodied experience.</p><p> Figure 1a shows the relation among the current skills and competences a cartographer needs. In the center of the triangle the map and the cartographic method. Data, Media and Users are found around. Knowledge and skills about data handling refer to selection, integration and abstraction, as well as analysis. Media skills and knowledge are about the interface, interaction, adapted design, technology and coding. Users refers to usability (enjoyment), cognition, perception, sensors (robots) and requirements. In Figure 1b the changing paradigm of the map as interface between human and reality as seen yesterday, today and tomorrow.</p><p> How do we do it? Our MSc Cartography:</p><p>The Erasmus Mundus Master of Science in Cartography program is characterized by its worldwide unique profile and comprehensive and in-depth cartographic lectures and lab works. All four partner universities (see involved authors) jointly developed and defined the learning outcomes after intensive cooperation and consultation. The program takes all theoretical as well as practical aspects of the broad and interdisciplinary field of cartography into account. Graduates of the program are able to meet the variety of requirements placed on a cartographer today. </p><p>An obvious strength of this program is the clear research-driven orientation of selected lectures, e.g. visual analytics, web and mobile cartography and the close binding of M.Sc. topics to ongoing research projects. Students in the Cartography program learn how to develop and evaluate cartographic tools on the basis of firmly established theories and methods. The focus lays in developing and applying scientific methods and techniques to improve geo-information services for a diverse range of heterogeneous users.</p><p> Another added value of the program is its educational execution in locations across Europe, a historic center of excellence in the field of cartography, integrating it within interdisciplinary fields. Excellently educated students from this program will fill the gaps not only in the cartographic research community and geosciences, but also in other related research fields that address the global challenges as defined by bodies like the United Nations or the European Union.</p>

North America’s midcontinent forest–prairie ecotone is currently exhibiting extensive eastern redcedar (ERC) (Juniperus virginiana L.) encroachment. Rapid expansion of ERC has major impacts on the species composition and forest structure within this region and suppresses previously dominant oak (Quercus) species. In Kansas, the growing-stock volume of ERC increased by 15,000% during 1965–2010. The overarching goal of this study was to evaluate the spatio-temporal dynamics of ERC in the forest–prairie ecotone of Kansas and understand its effects on deciduous forests. This was achieved through two specific objectives: (i) characterize an effective image classification approach to map ERC expansion, and (ii) assess ERC expansion between 1986 and 2017 in three study areas within the forest–prairie ecotone of Kansas, and especially expansion into deciduous forests. The analysis was based on satellite imagery acquired by Landsat TM and OLI sensors during 1986–2017. The use of multi-seasonal layer-stacks with a Support Vector Machine (SVM)-supervised classification was found to be the most effective approach to classify ERC distribution with high accuracy. The overall accuracies for the change maps generated for the three study areas ranged between 0.95 (95 CI: ±0.02) and 0.96 (±0.03). The total ERC cover increased in excess of 6000 acres in each study area during the 30-year period. The estimated percent increase of ERC cover was 139%, 539%, and 283% for the Tuttle Creek reservoir, Perry reservoir, and Bourbon County north study areas, respectively. This astounding rate of expansion had significant impacts on the deciduous forests where the conversion of deciduous woodlands to ERC, as a percentage of the total encroachment, were 48%, 56%, and 71%, for the Tuttle Creek reservoir, Perry reservoir, and Bourbon County north study areas, respectively. These results strongly affirm that control measures should be implemented immediately to restore the threatened deciduous woodlands of the region.

Objective: The purpose of this article was to provide static and interact mapping for the results' SaTscan with R package thereby reduce the gap between decision-makers and researchers.Introduction: Scan statistics is one of the most widely used method for detecting and locating the clusters of disease or health-related events through the space-time dimension. Although the Specific software of SatScan is available for free and easier to use graphical user interface (GUI) interface, the click way and the resulting text format have became obstacles in biosurveillance since automated or reproduction operation and the fast communicate information tool appeared. With the power of R software and ‘rsatscan’ package, we extended the visualization results to become a faster, more effective communication and motivation tool.Methods: All the data are from a syndromic surveillance and real-time early warning system, which established 3 counties in the Yunnan province in the China for improving the ability to handle public health emergencies events and reduce the potential risk of disease spread. To illustrate the purpose of visualization, we only use one county data from 2017/9/1 to 2017/9/30 which includes two data sources: primary schools’ absentees and health clinics’ records. Based on the ‘rsatscan’ package which makes it easy to work within SaTscan from R, we developed three ways for the results of Spatio-temporal scan: traditional tables, static maps and interactive visualize maps. Especial the last interactive visualization benefits from dynamic queries which may be an incredible tool to explore potential “clusters” data.Data are collected from web-based by smart-phone or internet including 83 health clinics and 118 primary schools for one month. All the programs are run on Rstudio. The retrospective spatio-temporal scan parameters for two data sources as follow: Analysis type=retrospective Space-Time, Analysis type=Space-Time Permutation, Model Type= High Rates (Poisson), Time precision=day, Time aggregation units=day, MaxSpatialSizeInDistanceFromCenter=10 km, MaxTemporalSize=14 day.Results: 76211 records in the health clinics and 6066 absenteeism in the primary schools are collected. Three ways for the spatio-temporal scan results are provided in table (Tab-1), static maps (Fig 1) and interactive visualize maps online, some of them are presented in html format.The table shows two data sources results by stack ways. The first column is the order of most likely to cluster. Follow is the code for center of the circular scan. The remaining indicators include Time Interval, Risk Value, Observed and expected value, P values from 999 Montel Carlo Simulation. See the table in more details.The static maps have the advantages of vivid communicates information for where are the potential “cluster” both in two data sources over the space. What is more, one benefit of this way can provide the possible association between medical institution information and primary school absence information through the overlap circular.The most excited is the interactive visualization with HTML format. From the click the navigate widgets on the left top, you can choose different layers. If you want to know more cluster information by the different potential cluster, clicking the map or dots or circular, and the pop-up dialogue box will show with the related clusters results of scan statistics methods.See the detail in the website: http://rpubs.com/ynsxx/318257Conclusions: These innovation ways can provide the ability to process information faster and to use that information to boost productivity and results. It is easy to help decision-makers to visualize communicates information faster than traditional reports. And the R code will more suitable for prospective analysis.

Thousands of movies along with TV shows, documentaries are being produced each year around the world with different genres and languages. Making a movie scene impactful as well as original is challenging task for the director. On the other hand, users demands to retrieve similar scenes from their queries is also challenging task as there is no proper maintenance of database of movie scene videos with proper semantic tags associated with it. So to fulfill the requirement of these two (but not the least) application areas there is a need of content based retrieval system for movie scenes. Content based video retrieval is a problem of retrieving most similar videos to a given query video by analyzing the visual contents of videos. Traditional video level features based on key frame level hand engineered features which does not exploit rich dynamics present in the video. In this paper we propose a Content based Movie Scene Retrieval (CB-MSR) framework using spatio-temporal features learned by deep learning. Specifically deep CNN along with LSTM is deploy to learn spatio-temporal representations of video. On the basis of these learned features similar movie scenes can be retrieve from the collection of movies. Hollywood2 dataset is used to test the proposed system. Two types of features: spatial and spatio-temporal features are used to evaluate the proposed framework.