Spatial Data Analytics Research Articles

TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping

The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil, PRODES and DETER have shown that they are committed to monitoring the mapping areas of large-scale deforestation systematically and by means of data quality assurance. However, these programs are so complex that they require the designing, implementation and deployment of a spatial data infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. With this in mind, the Brazilian National Institute for Space Research (INPE) has designed TerraBrasilis, a spatial data analytics infrastructure that provides interfaces that are not only found within traditional geographic information systems but also in data analytics environments with complex algorithms. To ensure it achieved its best performance, we leveraged a micro-service architecture with virtualized computer resources to enable high availability, lower size, simplicity to produce an increment, reliable to change and fault tolerance in unstable computer network scenarios. In addition, we tuned and optimized our databases both to adjust to the input format of complex algorithms and speed up the loading of the web application so that it was faster than other systems.

Geospatial Dashboards for Monitoring Smart City Performance

Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.

A Review on 3D Spatial Data Analytics for Building Information Models

Except for various properties, accurate and intuitive 3D representations of building elements and spaces are embedded in Building Information Models (BIMs). Thus, better understanding of spatial features of building elements and spaces is possible, which brings new opportunity in code compliance checking, indoor navigation, visualization, etc. With large amount of fine-grained 3D spatial data collected quickly and continuously, there is an urgent need for new methods to overcome problems like improper spatial expression, insufficient mining and utilization of information that exist in traditional methods. This research reviews state-of-the-art of related research and provides a summary of achievements and challenges in this area. Then, a framework consists of management, analysis, and application of 3D spatial data of BIM and a detailed discussion of each part are proposed, which would give the readers an overview of relevant methods, technologies, and tools. Moreover, potential research directions and open issues for future work are also discussed. The paper enables researchers to get a comprehensive understanding of 3D spatial data analytics of BIM as well as suggestions for future work, and thus makes a solid contribution in this area.

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.

Bridging open source tools and Geoportals for interactive spatial data analytics

ABSTRACTGeoportals have been the primary source of spatial information to researchers in diverse fields. Recent years have seen a growing trend to integrate spatial analysis and geovisual analytics inside Geoportals. Researchers could use the Geoportal to conduct basic analysis without offline processing. In practice, domain-specific analysis often requires researchers to integrate heterogeneous data sources, leverage new statistical models, or build their own customized models. These tasks are increasingly being tackled with open source tools in programming languages such as Python or R. However, it is unrealistic to incorporate the numerous open source tools in a Geoportal platform for data processing and analysis. This work provides an exploratory effort to bridge Geoportals and open source tools through Python scripting. The Geoportal demonstrated in this work is the Urban and Regional Explorer for China studies. A python package is provided to manipulate this platform in the local programming environment. The server side of the Geoportal implements a set of service endpoints that allows the package to upload, transform, and process user data and seamlessly integrate them into the existing datasets. A case study is provided that illustrated the use of this package to conduct integrated analyses of search engine data and baseline census data. This work attempts a new direction in Geoportal development, which could further promote the transformation of Geoportals into online analytical workbenches.

Welcome to a New Generation of Entertainment: Amazon Web Services and the Normalization of Big Data Analytics and RFID Tracking

The 2017 partnership between the National Football League (NFL) and Amazon Web Services (AWS) promises novel forms of cutting-edge real-time statistical analysis through the use of both radio frequency identification (RFID) chips and Amazon’s cloud-based machine learning and data-analytics tools. This use of RFID is heralded for its possibilities: for broadcasters, who are now capable of providing more thorough analysis; for fans, who can experience the game on a deeper analytical level using the NFL’s Next Gen Stats; and for coaches, who can capitalize on data-driven pattern recognition to gain a statistical edge over their competitors in real-time. In this paper, we respond to calls for further examination of the discursive positionings of RFID and big data technologies (Frith 2015; Kitchin and Dodge 2011). Specifically, this synthesis of RFID and cloud computing infrastructure via corporate partnership provides an alternative discursive positioning of two technologies that are often part of asymmetrical relations of power (Andrejevic 2014). Consequently, it is critical to examine the efforts of Amazon and the NFL to normalize pervasive spatial data collection and analytics to a mass audience by presenting these surveillance technologies as helpful tools for accessing new forms of data-driven knowing and analysis.

Spatial and big data analytics of E-market transaction in China

This study uses a big data approach and gravity model to quantify the scope and sources of online transactions in urban China and explore the driving forces, based on data from the Taobao platform for online cellphone transactions from June to December in 2011. Comparison among Jing-Jin-Ji Region, Yangtze River Delta, and Pearl River Delta shows that a higher level of economic development corresponds to the more developed logistics industry and more C2C Taobao shops. The regression results illustrate that distance, GDP, and population density are the three main factors which influence the volume and number of trades in the e-marketplace. The number and reputation of traders by relative value also promote the volume and numbers of trades significantly. Additionally, the big data from the Taobao platform provides evidence that the gravity model is valid in estimating the amounts of online transactions.

Using spatial data analytics to identify associations between home healthcare services’ accessibility and socioeconomic factors

Home healthcare can improve health outcomes and reduce healthcare costs. However, realizing its benefits is only possible if the service is accessible for patients who need it. Thus, evaluating and understanding the inequities in accessibility of home healthcare services are necessary to improve the healthcare system and overall population health. This article utilizes detailed healthcare system and socioeconomic data in a spatial statistical modeling framework, from which we make inference about spatially explicit relationships between home healthcare accessibility and socioeconomic factors, including rural/urban status, income, education, and race/ethnicity. This is accomplished via space-varying coefficient models that account for the spatial autocorrelation in both the response and predictor variables, as well as capture spatial heterogeneity in the relationships between accessibility and socioeconomic factors. We find statistically significant and spatially varying relationships between accessibility of home healthcare services and several socioeconomic variables at the zip code level. Our results have the potential to inform policies aimed at addressing disparities in spatial accessibility, promoting healthcare effectiveness, and improving outcomes. This work makes important contributions to improving healthcare outcomes using a spatial analytics framework that integrates healthcare systems data at a fine spatial scale with advanced statistical methods.

Spatial data analytics of mobility with consumer data

Consumer data arising from the interaction between customers and service providers are becoming ubiquitous. These data are appealing for research because they are frequently collected and quickly released; they cover a wide variety of attitudes, lifestyles and behavioural characteristics; and they are often dynamically replenished and longitudinal. It is demonstrated that consumer data can make important contributions to understanding problems in transport geography and in solving applied problems ranging from migration, infrastructure investment and retail service provision to commuting and individual mobility. However more effective exploitation of these data depends on the construction of bridges to allow greater freedom in the transfer of data from the commercial to the academic sector; it requires development of frameworks for privacy and ethics in the secondary use of personal data; and it is contingent on the emergence of effective strategies for the amelioration of selection bias which impairs the quality of many consumer data sources.

Remote Sensing Big Data: Theory, Methods and Applications

Nowadays, our ability to acquire remote sensing data has been improved to an unprecedented level.[...]

Mining significant crisp-fuzzy spatial association rules

ABSTRACTSpatial association rule mining (SARM) is an important data mining task for understanding implicit and sophisticated interactions in spatial data. The usefulness of SARM results, represented as sets of rules, depends on their reliability: the abundance of rules, control over the risk of spurious rules, and accuracy of rule interestingness measure (RIM) values. This study presents crisp-fuzzy SARM, a novel SARM method that can enhance the reliability of resultant rules. The method firstly prunes dubious rules using statistically sound tests and crisp supports for the patterns involved, and then evaluates RIMs of accepted rules using fuzzy supports. For the RIM evaluation stage, the study also proposes a Gaussian-curve-based fuzzy data discretization model for SARM with improved design for spatial semantics. The proposed techniques were evaluated by both synthetic and real-world data. The synthetic data was generated with predesigned rules and RIM values, thus the reliability of SARM results could be confidently and quantitatively evaluated. The proposed techniques showed high efficacy in enhancing the reliability of SARM results in all three aspects. The abundance of resultant rules was improved by 50% or more compared with using conventional fuzzy SARM. Minimal risk of spurious rules was guaranteed by statistically sound tests. The probability that the entire result contained any spurious rules was below 1%. The RIM values also avoided large positive errors committed by crisp SARM, which typically exceeded 50% for representative RIMs. The real-world case study on New York City points of interest reconfirms the improved reliability of crisp-fuzzy SARM results, and demonstrates that such improvement is critical for practical spatial data analytics and decision support.

Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China

Housing is among the most pressing issues in urban China and has received considerable scholarly attention. Researchers have primarily concentrated on identifying the factors that influence residential property prices and how such mechanisms function. However, few studies have examined the potential factors that influence housing prices from a big data perspective. In this article, we use a big data perspective to determine the willingness of buyers to pay for various factors. The opinions and geographical preferences of individuals for places can be represented by visit frequencies given different motivations. Check-in data from the social media platform Sina Visitor System is used in this article. Here, we use kernel density estimation (KDE) to analyse the spatial patterns of check-in spots (or places of interest, POIs) and employ the Getis-Ord method to identify the hot spots for different types of POIs in Shenzhen, China. New indexes are then proposed based on the hot-spot results as measured by check-in data to analyse the effects of these locations on housing prices. This modelling is performed using the hedonic price method (HPM) and the geographically weighted regression (GWR) method. The results show that the degree of clustering of POIs has a significant influence on housing values. Meanwhile, the GWR method has a better interpretive capacity than does the HPM because of the former method’s ability to capture spatial heterogeneity. This article integrates big social media data to expand the scope (new study content) and depth (study scale) of housing price research to an unprecedented degree.

Enhancement of Effective Spatial Data Analysis using R

Background: The availability of Spatial Data which is a part of GIS is growing day by day in an exponential manner. This high availability of data is throwing challenges to the research community to analyze and draw effective conclusions. The Present Study aims at requirement for effective analysis and to draw Conclusions. Methods/Statistical Analysis: Spatial Analysis requires logical relationships between attribute data and map features.Spatial data Analysis is not a simple single task it requires complex procedures in which combinational techniques namely Hybrid techniques are required for effective analysis. Mathematics and statistics are the fundamentals to spatial data analytics. In this paper, a realistic Spatial crime data set was considered for analysis. It involves different types of data mining Techniques like Clustering, Classification and Association rule mining techniques apart from Hybrid techniques. These hybrid Data mining Techniques were applied using R. Findings: The Hybrid Data Mining techniques with K-means Clustering and J48 Decision Tree Algorithm was developed and Applied for the enhancement of accuracy. Association Rule generation Apriori algorithm was applied on the resultant K-means clustered data set. The application of 3D visualization techniques also made for further analysis. Applications/Improvements: It is essentially required to analyze these complex spatial data sets effectively. So there is a need of hybrid Spatial Data Mining Techniques requirement for effective analysis and to draw Conclusions.

Spatiotemporal Analysis of Housing Prices in China: A Big Data Perspective

Due to the rapid economic growth and urbanization, China’s real estate industry has been undergoing a fast-paced development in recent decades. However, the spatial imbalance between the economic growth in urban and that in rural areas and the excessive growth and fluctuations of house prices in both areas had quickly caught public’s attention. Not surprisingly, these issues had become a focus of urban and regional economic research. Efficient and accurate prediction of housing prices remains a much needed but disputable topic. Currently, based on the trends and changes in the financial market, population migration and urbanization processes, numerous case studies have been developed to evaluate the mechanism of real estate’s price fluctuations. However, few studies were conducted to examine the space-time dynamics of how housing prices fluctuated from a big data perspective. Using data from China’s leading online real estate platform {sofang.com}, we investigated the spatiotemporal trends of the fluctuations of housing prices in the context of big data. This paper uses spatial data analytics and modeling techniques to: first, identify the spatial distribution of housing prices at micro level; second, explore the space-time dynamics of residential properties in the market; and third, detect if there exist geographic disparity in terms of housing prices. Results from our analysis revealed the space-time patterns of the housing prices in a large metropolitan area, demonstrating the utility of big data and means of analyzing big data.

The SpatialARMED Framework: Handling Complex Spatial Components in Spatial Association Rule Mining

Recent research has identified spatial association rule (SAR) mining as a promising technique for geographic pattern mining and knowledge discovery. Nevertheless, important spatial components embedded in the studied phenomenon, in particular complex spatial functional relations such as neighborhood effects and spatial spillover effects have largely been neglected. This article unravels this specific problem to enhance the effective application of SAR mining practices in spatial data analytics. The main discussion focuses on the specification of complex spatial components by means of spatial dependence properties of the data and on how to integrate them in the process of SAR mining. A comprehensive framework dubbed SpatialARMED is proposed for the effective extraction of spatial patterns. The framework is then showcased through its application to crime analysis.

Applied CyberGIS in the Age of Complex Spatial Health Data

GIScience 2016 Short Paper Proceedings Applied CyberGIS in the Age of Complex Spatial Health Data M. M. Jankowska 1 , J. Schipperijn 2 , J. Kerr 1 , I. Altintas 1 University of California San Diego, 9500 Gilman Drive La Jolla CA 92093-0811 USA Email: {majankowska; jkerr}@ucsd.edu; altintas@sdsc.edu University of Southern Denmark, Campusvej 55 Odense 5230 Denmark Email: jschipperijn@health.sdu.dk Abstract Advances in data acquisition in a number of fields throughout the health spectrum are resulting in large, complex, and diverse data sets. With proliferation of sensor and spatial data acquisition, the analysis and processing of complex spatial health data analytics will become a pressing problem. CyberGIS can offer solutions in this realm, however no systems have been developed that cater to the specific challenges associated with complex spatial health data such as privacy, real-time analytics, data standardization, data integration, workflow provenance, and a front end interface that is accessible to individuals in the public health realm. In this paper we present SPACES, an in-development CyberGIS to address some of these challenges. We discuss the architecture, and present two test case examples of utilizing SPACES for understanding environmental influences on physical activity. 1. Introduction Public health, health care, and medical advances are increasingly looking to the collection of voluminous data sets. Movements such as Quantified Self, where individuals engage in self- tracking of biological, physical, behavioural, and environmental information (Fawcett 2015), or the Precision Medicine Initiative Cohort announced by President Obama in 2015, which will include extensive data collection and tracking of over one million U.S. participants (Ashley 2015), are driving the need to develop cyberinfrastructures and methodologies for big and complex spatial health data integration, processing, and analysis. Complex spatial health data may include temporally-linked objective measures of behavior and health, molecular data such as genomics, proteomics, or metabolomics, life course data including movement trajectories, self-reported survey and demographic data, environmental data assessing exposures, and finely resolved spatial data. CyberGIS may assist in addressing the challenges of complex spatial health dat. It represents a new-generation GIS based on the synthesis of advanced cyberinfrastructure, geographic information science, and spatial analysis and modeling (Wang 2010). The field of CyberGIS is rapidly developing and the US National Science Foundation funded a 5-year major initiative titled ‘CyberGIS Software Integration for Sustained Geospatial Innovation’ (http://cybergis.org). However, advances in the application of CyberGIS to health specific problems are lacking. Goldberg et al. (2013) presented a comprehensive vision of a Spatial- Health CyberGIS Marketplace, which tackled many of the opportunities as well as challenges of a health focused CyberGIS including confidentiality and privacy protections, real-time analytic methods, data standardization, and a comprehensive end-to-end ecosystem architecture. We would add to this list the need for shareable workflows to promote inter- field collaboration, diverse data type integration, and replicability of analytic processes. Recently, the NSF funded a platform called DELPHI (Data E-platform for personalized population health) (Katsis et al. 2013), one of the first cyberinfrastructures in development specifically catered toward meeting the goals of integrating complex and diverse health data

The CASE histogram: privacy-aware processing of trajectory data using aggregates

Due to the high uptake of location-based services (LBSs), large spatio-temporal datasets of moving objects’ trajectories are being created every day. An important task in spatial data analytics is to service range queries by returning trajectory counts within a queried region. The question of how to keep an individual user’s data private whilst enabling spatial data analytics by third parties has become an urgent research direction. Indeed, it is increasingly becoming a concern for users. To preserve privacy we discard individual trajectories and aggregate counts over a spatial and temporal partition. However the privacy gained comes at a cost to utility: trajectories passing through multiple cells and re-entering a query region, lead to inaccurate query responses. This is known as the distinct counting problem. We propose the Connection Aware Spatial Euler (CASE) histogram to address this long-standing problem. The CASE histogram maintains the connectivity of a moving object path, but does not require the ID of an object to distinguish multiple entries into an arbitrary query region. Our approach is to process trajectories offline into aggregate counts which are sent to third parties, rather than the original trajectories. We also explore modifications of our aggregate counting approach that preserve differential privacy. Theoretically and experimentally we demonstrate that our method provides a high level of accuracy compared to the best known methods for the distinct counting problem, whilst preserving privacy. We conduct our experiments on both synthetic and real datasets over two competitive Euler histogram-based methods presented in the literature. Our methods enjoy improvements to accuracy from 10 % up to 70 % depending on trip data and query region size, with the greatest increase seen on the Microsoft T-Drive real dataset, representing a more than tripling of accuracy.

Introduction to this special issue

Nowadays, the digital traces of moving objects, such as people, animals, goods, increasingly contain richer information than the mere location history. Traces can report, for example, the places visited by tourists during a touristic journey (e.g., hotels, entertainment spots), the activities performed during such a journey (e.g., shopping, driving), the people encountered during the trip. This supplementary information is often referred to as semantic annotation (of trajectories). Semantic annotations can be directly acquired from sensors or, alternatively, be the result of some analytic process or be directly provided by users. In recent times, following a few pioneering research projects, especially in Europe and Asia, the processing of large amounts of semantics-enriched trajectories has become an exciting, cross-disciplinary research area, spanning spatial computing, data analytics and geographical information science. Semantics-enriched trajectories have important applications in a variety of domains, such as urban computing, social media analysis and animal ecology, especially in connection with the analysis of moving objects' behavior, both at individual and collective level.