Spatial-temporal Database Research Articles

FSOLAP: A fuzzy logic-based spatial OLAP framework for effective predictive analytics

Nowadays, with the rise in sensor technology, the amount of spatial and temporal data increases day by day. Fast, effective, and accurate analysis and prediction of collected data have become more essential than ever. Spatial Online Analytical Processing (SOLAP) emerged to perform data mining on spatial and temporal data that naturally contains the hierarchical structure used in many complex applications. In addition, uncertainty and fuzziness are inherently essential elements of data in many complex data applications, particularly in spatial–temporal database applications. In this study, FSOLAP is proposed as a new fuzzy SOLAP-based framework to compose the benefits of fuzzy logic and SOLAP concepts and is extended with inference capability to the framework to support predictive analytics. The predictive accuracy and resource utilization performance of FSOLAP are compared using real data with some well-known machine learning techniques such as Support Vector Machine, Random Forest, and Fuzzy Random Forest. The extensive experimental results show that the FSOLAP framework for the predictive analytics of various spatiotemporal events in big meteorological databases is considerably more accurate and scalable than using conventional machine learning techniques.

Spatio-temporal modelling of asthma-prone areas using a machine learning optimized with metaheuristic algorithms

The main aim of this study was to use two metaheuristic optimization algorithms—a genetic algorithm (GA) and a teaching-learning-based optimization (TLBO) algorithm—to determine the optimal parameters of a support vector regression (SVR) model for Spatio-temporal modelling of asthma-prone areas in Tehran, Iran. First, a spatial-temporal database consisting of dependent (872 patients with asthma) and independent data (air pollution, meteorology, distance to park, and street parameters) was created. In the next step, Spatio-temporal modelling and mapping of asthma-prone areas were performed using three models: SVR, SVR-GA, and SVR-TLBO. The highest accuracy of the area under the curve (AUC) of the receiver operating characteristic (ROC) was for SVR-GA (0.806, 0.801, 0.823, and 0.811), SVR-TLBO (0.8, 0.797, 0.81, and 0.803), and SVR (0.786, 0.78, 0.796, and 0.791) models in spring, summer, autumn, and winter, respectively. Autumn, winter, spring, and summer were most accurate in modelling asthma occurrence, respectively.

Landscape Dynamics (landDX) an open-access spatial-temporal database for the Kenya-Tanzania borderlands

The savannas of the Kenya-Tanzania borderland cover >100,000 km2 and is one of the most important regions globally for biodiversity conservation, particularly large mammals. The region also supports >1 million pastoralists and their livestock. In these systems, resources for both large mammals and pastoralists are highly variable in space and time and thus require connected landscapes. However, ongoing fragmentation of (semi-)natural vegetation by smallholder fencing and expansion of agriculture threatens this social-ecological system. Spatial data on fences and agricultural expansion are localized and dispersed among data owners and databases. Here, we synthesized data from several research groups and conservation NGOs and present the first release of the Landscape Dynamics (landDX) spatial-temporal database, covering ~30,000 km2 of southern Kenya. The data includes 31,000 livestock enclosures, nearly 40,000 kilometres of fencing, and 1,500 km2 of agricultural land. We provide caveats and interpretation of the different methodologies used. These data are useful to answer fundamental ecological questions, to quantify the rate of change of ecosystem function and wildlife populations, for conservation and livestock management, and for local and governmental spatial planning.

Where Truck Drivers Stop – Application of Vehicle Tracking Data for the Identification of Rest Locations and Driving Patterns

Road transport plays an essential role in freight transport throughout Europe, therefore, conditions that may hinder seamless operations in this sector require thorough consideration for evidence-based action. Critical amongst these key conditions is how, when, and where truck drivers stop, as a common set of rules strictly regulates their driving times and rest periods, which causes mandatory interruptions in the supply chains. However, approximating reliable estimations of freight traffic flows and road infrastructure usage constitutes a considerable challenge for researchers. This paper presents a robust data processing approach to designate rest area stops and to calculate the pertaining driving and rest times. Drawing on the abundance of navigation information provided by private fleet toll registration services, a comprehensive spatial-temporal truck stop database on all major rest areas along the toll road network in Hungary has been compiled. Based on the assessment and comparison of driving and rest times, driving and parking times have been analysed, including micro-scale analysis of particular rest areas. Both the methods applied and the results achieved can be of strategic interest to better understand truck driving patterns, as well as to develop targeted and cost-effective measures to streamline freight transport operations in other contexts.

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

SPATIAL DIFFERENCES IN FRESH VEGETABLE SPENDING: A CASE STUDY IN GUILFORD COUNTY, NORTH CAROLINA

Abstract. This paper investigates the spatial differences in fresh vegetable spending in Guilford County, North Carolina. We create a geo-coded spatial-temporal database for both human factors and natural factors to understand why food deserts have become a serious issue in a county with many farming activities. We find that residents living in food deserts do not buy enough fresh vegetables compared with their counterparts, even when they are shopping at full-service grocery stores. Social-economic factors are most sensitive and are important determinants of fresh food demand. Using an agent-based toy model, we find that fresh vegetable demand in each census tract in Guilford County varies to a large extent. The results suggest that the formation of food deserts may root from the demand side.

Spatial-Temporal Characteristic Analysis of Ethnic Toponyms Based on Spatial Information Entropy at the Rural Level in Northeast China.

As a symbol language, toponyms have inherited the unique local historical culture in the long process of historical development. As the birthplace of Manchu, there are many toponyms originated from multi-ethnic groups (e.g., Manchu, Mongol, Korean, Hui, and Xibe) in Northeast China which possess unique cultural connotations. This study aimed to (1) establish a spatial-temporal database of toponyms in Northeast China using a multi-source data set, and identify their ethnic types and origin times; and (2) explore the geographical distribution characteristics of ethnic toponyms and the evolution of rural settlements by comparing the spatial analysis and spatial information entropy methods. The results found that toponyms reflect not only the spatial distribution characteristics of the density and direction of ethnic groups, but also the migration law of rural settlements. Results also confirm that toponyms contain unique cultural connotations and provide a theoretical basis for the protection and promotion of the cultural connotations of toponyms. This research provides an entropic perspective and method for exploring the spatial-temporal evolutionary characteristics of ethnic groups and toponym mapping.

Active acquisition system of LBS-based logistics freight source information

In order to solve the bottleneck of logistics information exchange and meet the demand for getting freight source information in time, this paper proposes a solution of active acquisition system of logistics sources information based on LBS with Android mobile terminal. By using GPS/WIFI positioning technology to achieve the acquisition of the information of user's current location and through wireless network, the data are further sent to the spatialtemporal database of service terminal, where the spatial data and attribute data about logistics sources information are integrated and stored. Then these data are analysed and processed by use of the dynamic programming algorithm based on real-time logistics sources information. Finally the Baidu API is used to realise the visualisation of post-processing of logistics sources information so that the services in active acquisition of peripheral logistic information and optimal receiving route planning might be provided.

Development of Spatio-Temporal Database Prototype For Management of 4-Dimensional Cadastre Object In Indonesia

The dynamics of the Indonesian population has increased causing the need for cadastral objects to increase. 2D and 3D cadastral objects change over time. Cadastral objects have spatial and juridical data changes every day. These changes can be caused by the transfer of rights, changes of rights, split and merging cadastral objects. Changes are recorded and stored as a history of cadastral objects (4-dimensional cadastre). Historical data on cadastral objects can be categorized as one of the big data in the cadastre sector, based on the frequency of recording data. The data can be used to track changes in cadastral objects so that they can avoid disputes. Spatial data in this case is very vulnerable data to cause disputes if the shape, position and size do not match the conditions in the field, and need special attention in integrating with the juridical data. This paper aims to identify methods for storing spatial data of 4D cadastral objects that are suitable for Indonesia. The importance of storing cadastral objects and their history causes researchers and cadastre experts in the world to formulate international standards in managing spatial and juridical data along with a history of cadastral objects (4D cadastre). LADM is an international standard conceptual model (ISO 19152) which can show the legacy of a cadastral object expressed in RRR (Rights, Restriction and Responsibility). In LADM, there is a VersionedObject class that represents various versions or a history of spatial units, object registration, owner and administration. Several studies conducted by researchers in the world were compared in this paper to find methods for applying LADM and VersionedObject classes. That methods were implemented in this paper to design a database of spatio-temporal 4D cadastre. Finally a prototype of spatial-temporal database will be produced to manage 4D cadastral objects.

Mining frequent trajectory patterns of WIP in Internet of Things-based spatial-temporal database

The application of Internet of Things technologies has led to a data-rich manufacturing environment by connecting manufacturing objects as a collaborative community. However, advanced analytics approach is comparatively inadequate for work-in-process (WIP) trajectory data. On the other hand, although the topic of mining frequent trajectory patterns has raised a great deal of attention, it mainly focuses on the fields of vehicle traffic management and users’ behaviours. When applied in manufacturing shop floor, the extracted knowledge is physical trajectory patterns and lacks manufacturing significance. This paper manages to obtain logical knowledge with manufacturing significance from WIP trajectory data. In this paper, a data model is introduced to map physical trajectories of WIP into logical space, in order to capture logical features of manufacturing system. Moreover, an algorithm named PMP is proposed to extract logical trajectory patterns. Several experiments are conducted to examine the performance. The results prove the efficiency and feasibility of the proposed method.

Geospatial characterization of building material stocks for the life cycle assessment of end-of-life scenarios at the urban scale

Demolition waste represents a significant portion of the total generated waste and has a high importance from both a waste management and a resource efficiency perspective. The urban context is highly relevant to assess the environmental impact of the end-of-life stage of buildings and potential for future reduction to properly design corresponding demolition waste management strategies.The goal of this paper is the development of a framework for the characterization of building material stocks and the assessment of the potential environmental impact associated with the end-of-life of buildings at the urban scale to support decision on waste management strategies. The methodology combines a bottom-up material stock model based on geographical information systems (GIS) and a spatial–temporal database with life cycle assessment (LCA) for the evaluation of end-of-life scenarios.The approach was tested for the city of Esch-sur-Alzette (Luxembourg) and provided significant results on the quantity and the composition of the housing material stock. Two alternative scenarios involving recycling rates of respectively 50% and 70% for inert materials were assessed and an average reduction potential of 25.6% on abiotic depletion potential and 9.2% on global warming potential was estimated.

Detecting spatial-temporal clusters of HFMD from 2007 to 2011 in Shandong Province, China.

BackgroundHand, foot, and mouth disease (HFMD) has caused major public health concerns worldwide, and has become one of the leading causes of children death. China is the most serious epidemic area with a total of 3,419,149 reported cases just from 2008 to 2010, and its different geographic areas might have different spatial epidemiology characteristics at different spatial-temporal scale levels. We conducted spatial and spatial-temporal epidemiology analysis to HFMD at county level in Shandong Province, China.MethodsBased on the China National Disease Surveillance Reporting and Management System, the spatial-temporal database of HFMD from 2007 to 2011 was built. The global autocorrelation statistic (Moran’s I) was first used to detect the spatial autocorrelation of HFMD cases in each year. Purely Spatial scan statistics combined with Space-time scan statistic were used to detect epidemic clusters.ResultsThe annual average incidence rate was 93.70 per 100,000 in Shandong Province. Most HFMD cases (93.94%) were aged within 0–5 years old with an average male-to-female sex ratio 1.71, and the incidence seasonal peak was between April and July. The dominant pathogen was EV71 (47.35%), and CoxA16 (26.59%). HFMD had positive spatial autocorrelation at medium spatial scale level (county level) with higher Moran’s I from 0.31 to 0.62 (P<0.001). Seven spatial-temporal clusters were detected from 2007 to 2011 in the landscape of the whole Shandong, with EV71 or CoxA16 as the dominant pathogen for most hotspots areas.ConclusionsThe spatial-temporal clusters of HFMD wandered around the whole Shandong Province during 2007 to 2011, with EV71 or CoxA16 as the dominant pathogen. These findings suggested that a real-time spatial-temporal surveillance system should be established for identifying high incidence region and conducting prevention to HFMD timely.

Spatio-Temporal Database and Its Models: A Review

This paper focus on the study of spatial temporal database and its models and different types of applications where dynamic modeling of spatial temporal database can be used, this is an emerging field which places lot of contribution in DBMS with the aspects of the real world. Spatio-Temporal data models are the heart of a Spatio-Temporal Information System (STIS); they describe object data types, relationships, operations and rules to maintain database integrity. A rigorous data model must anticipate spatio-temporal queries and analytical methods to be performed in the STIS. Spatio-temporal database models are proposed to deal with real world applications, where spatial changes occur over the time line. A serious weakness of existing models is that each of them deals with few common characteristics found across a number of specific applications. Thus the applicability of the model to different cases, fails on spatio-temporal behaviors not anticipated by the application used for the initial model development. Keywords- Land Information System (LIS), Geographical Information System (GIS), Spatial-temporal databases, STIS.

Moving Object in Different Environment (MODE) using Spatial-Temporal Database Concept

temporal data models and query languages are a topic of growing interest. A spatial-temporal database is a database that embodies spatial, temporal, and spatial-temporal database concepts and captures simultaneously spatial and temporal aspects of data. A spatial data object occupies a certain region of space, which is characterized by its location and boundary. It deals with geometries that change with time. In this research to focus the moving object in different environment with the algorithmic details of each object movement likewise sky, water fall and hills. The positions of moving objects are continuously changing over time. For modeling these moving objects, consider both continuous and discrete models. The position of moving object can be calculated by time, speed, route, hiding position and reflection angle etc. The speed and route are managed as dynamic attributes, and are the motion information of moving object. The attribute uses in whole concept in dynamic. KeywordsTemporal Database-Moving Object- Hiding Value- Location finding-Query analyzation

Radio environment map-based cognitive Doppler spread compensation algorithms for high-speed rail broadband mobile communications

Recently, there is an increasing yet challenging demand on broadband mobile communications for high-speed trains. In this article, cognitive Doppler spread compensation algorithms are proposed for high-speed rail broadband mobile communications, which make use of the dedicated radio environment map (REM) for railway to compensate the time-varying Doppler spread. The dedicated REM for high-speed rail can be viewed as a spatial-temporal database consisting of the radio channel parameters along a given railway. The performance of the proposed Doppler spread compensation algorithms are evaluated with a typical OFDM-based broadband mobile system. Simulation results show that the link-level performance of high-speed rail broadband mobile communications can be improved significantly due to the REM-enabled radio channel condition awareness and the cognitive Doppler spread compensation algorithms. The REM-based cognitive radio approach presents a new paradigm for systems design of high-speed rail broadband mobile communications.

Hydrogeological mapping of the highly anthropogenically influenced Peligna Valley intramontane basin (Central Italy)

This paper presents hydrogeological mapping of the Peligna Valley (Central Italy) intramontane basin. The basin is hydrogeologically complex due to the significant surface and underground water resources which are extensively used for potable, industrial, agricultural and healing purposes. The basin history is characterised by significant fluvio-lacustrine deposition whose thickness can reach as much as 500 m. The depositional environment is completely surrounded by calcareous mountains. These represent extensive aquifers which feed large regional point and linear springs with total discharges of 16 m3/s. The highly complex hydrogeology of the study area and the varied use of water resources required extensive data collation and geological and hydrogeological assessments. Two years worth of hydrodynamic and hydrochemical monitoring of wells, springs, rivers and particularly artificial channels that convey water for industrial, hydroelectric and irrigational purposes were also undertaken. The construction of an accurate spatial-temporal database allowed the assessment and mapping of complex relationships between superficial water and groundwater. These relationships are shown in the 1:25,000 scale map. The map has been compiled using a detailed descriptive legend, various hydrogeological sections, schematics of anthropogenic activities, diagrams and tables and, finally, four schematics at 1:150,000 scale showing risk of contamination. It is hoped that this layout will assist in reading the map independently from any other supporting material.

Spatial-temporal Changes of the Land Use/Cover in the Dongting-Lake Area during the Last Twenty Years

The research on the land use/cover change is one of the frontiers and hot spots in the globe change research Based on the Chinese resource and environment spatialtemporal database, and the Landsat TM?MSS and ETM data which were acquired at the end of 1970's?1980's and 1990's respectively, we analyzed the spatialtemporal characteristics of land use/cover changes in the Dongting Lake area The result shows that during the last twenty years there were three landuse types that have changed remarkably The cultivated land decreased by 197% of total cultivated land The construction land and water area expanded, with an increase of 1488% and 877% respectively The landuse change speed of landuse type at the period between the end of 1970's and 1980's is slower than that at the period between the end of 1980's and 1990's Three cities and towns, Yueyang City?Yuanjiang County and Hanshou County have gotten the greatest value of the degree of landuse change in all administrative district at the previous 10 years, which means that the land use in these cities and towns has changed much quicklyHowever, at the later 10 years, Shishou City and Jinshi City, all of them are citylevel district,have developed from county to city, replacing Yuan-jiang County and Hanshou County, and have gotten the greatest value of the degree of landuse change

Mining frequent trajectory patterns in spatial–temporal databases

In this paper, we propose an efficient graph-based mining (GBM) algorithm for mining the frequent trajectory patterns in a spatial–temporal database. The proposed method comprises two phases. First, we scan the database once to generate a mapping graph and trajectory information lists (TI-lists). Then, we traverse the mapping graph in a depth-first search manner to mine all frequent trajectory patterns in the database. By using the mapping graph and TI-lists, the GBM algorithm can localize support counting and pattern extension in a small number of TI-lists. Moreover, it utilizes the adjacency property to reduce the search space. Therefore, our proposed method can efficiently mine the frequent trajectory patterns in the database. The experimental results show that it outperforms the Apriori-based and PrefixSpan-based methods by more than one order of magnitude.

Farms, fires, and forestry: Disturbance legacies in the soils of the Northwest Wisconsin (USA) Sand Plain

We studied the long-term effects of disturbance within the Northwest Wisconsin (USA) Sand Plain (NWSP), an ecoregion that is characterized by very sandy soil and an active disturbance history that includes fire, agriculture and industrial forestry, largely clearcut logging of jack pine ( Pinus banksiana) and aspen ( Populus spp.). Open “barrens” communities on this landscape were formerly maintained by fire, and are a high conservation priority. Hill's Oak ( Quercus ellipsoidalis) can also dominate forest canopies, while blueberry ( Vaccinium angustifolium), and sweetfern ( Comptonia peregrina) are common shrub species. We structured a field sampling design with a spatial-temporal database built from historic airphotos (1938 and 1997) and fire records to examine whether soil organic matter and nutrients vary with disturbance history in the nonforest habitats of the sand plain. We sampled soils along 83 transects, randomly stratified among five sampled classes: (1) nonforest-farming history; (2) nonforest-fire history; (3) nonforest-clearcut only history; (4) evergreen forest of jack pine and red pine ( P. resinosa); and (5) deciduous forest of Hill's oak and aspen. Logging of the original forest took place in the late 1800s–early 1900s. The farms were abandoned between 1938 and 1960, and the most recent fire occurred in 1977. Thus, the duration of the agricultural legacy is approximately 45–65 years while observed fire effects have lasted for 26 years. We observed strong agricultural legacies, including high P and low OM, N and Ca. One possible explanation for the N legacy is that it is tied to soil OM accretion which may be driven by plant growth. We detected no difference in mean values for any of the soil properties between soils from nonforested areas within the Five-Mile fire and soils from nonforested areas with a clearcut-only history. We did observe a fire effect in high variance for soil P. This could have resulted from variations in fire severity and ash convection and deposition. Forest soils generally had lower pH than the nonforest soils, and the deciduous forest soils had the lowest pH and also very low Ca. We also observed high within-transect coefficient of variation for Ca in the forest soils. We conclude that agriculture is a qualitatively different disturbance-type than fire or clearcutting, that disturbance legacies tend to be most persistent with geologically stable elements, such as P, and that management and conservation planning within the NWSP would benefit from site-specific agricultural history, as well as attention to Ca.

Pavement Marking Retroreflectivity

Previous research has shown that greater longitudinal pavement marking retroreflectivity levels increase drivers’ visibility and detection distance. However, increased visibility may cause drivers to feel too comfortable during nighttime conditions, and drivers may then pay less attention, operate their vehicles at unsafe speeds, or both. Before-and-after studies have been conducted on a pavement marking improvement such as repainting stripes or changing to a more durable marking material. Studies have also used models to estimate the retroreflectivity on the basis of the date of installation and vehicle exposure or have assumed a linear reduction in retroreflectivity over time. Only two studies have related field-measured pavement marking retroreflectivity to safety performance (crash data). One study analyzed the relationship between 3 years of pavement marking retroreflectivity data collected by the Iowa Department of Transportation on all state primary roads and corresponding crash and traffic data. The other study developed a spatial–temporal database by using measured retroreflectivity data to account for the deterioration of pavement markings over time, along with a statewide crash database to attempt to quantify a relationship between crashes and the quality (measured by retroreflectivity) of pavement marking. Three sets of data were analyzed: the complete database, records for two-lane roads, and records with retroreflectivity values less than or equal to 200 mcd/m2/lx only. The distributions and models of the entire database and the two-lane records did not show that poor pavement marking retroreflectivity correlated to a higher crash probability. However, when only records with retroreflectivity values of 200 mcd/m2/lx or less were examined, a statistically significant, albeit weak, relationship was determined.