Regional Policy Analysis in the Era of Spatial Big Data

Taking a byte out of big data

Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

The thesis concerns the use of big data in Official Statistics; the aim is to bring some new experimental studies into the big data literature. In particular, the purpose is to evaluate if and how big data could be used in Official Statistics. Only a few experiments exists on the use of big data for statistical purposes; it is a challenge task and a lot of experimentation is needed to find out evidence and solutions to use big data for statistical purposes. The analysis performed in the thesis goes into two different directions: 1. combining a traditional data source with a big data source to verify the potential of the latter to replicate official results; 2. analyzing a big data source per se and then trying to combine with an Official Statistics source to identify common patterns. The thesis initially proposes a literature review of definitions of big data and experiments, in particular concerning the use of the new sources combined with traditional data sources. Then, three original studies have been performed: the first two concern mobility in Lombardy region using mobile phone data. They both refer to the same issue (mobility patterns), but they differ in the traditional data source used: Origin/Destination matrix in the first case, an integrated version of the O/D matrix in the second. The objective of these two studies is trying to put in a unique interpretative framework one traditional statistical source and one typical kind of big data in order to evaluate some informative potentialities of this approach. In particular, we wanted to check if the two sources show common patterns, to evaluate future uses of the big data source in Official Statistics. The third study shows the pilot that was carried out during the traineeship I had the opportunity to attend at Eurostat, in collaboration with the Task Force Big Data. It concerns the use of Wikipedia, free online encyclopedia, for Tourism Statistics. The aim is to evaluate the use of Wikipedia page views as a source of information for the identification of factors that drive tourism to an area and whether it is possible to predict tourism flows using these data. A final chapter proposes conclusions and future remarks on the use of big data in Official Statistics. Two of the studies (the first on mobility patterns and the one on Wikipedia) have been or are being published, in a shorter and revised version. The three experiments show some potential in the use of big data in Official Statistics. The study needs more in-depth analysis, many more experiments and considerations will be necessary before we can achieve some definitive and convincing approaches.

After a disaster such as earthquakes, debris flows, forest fires, or landslides, etc., a lot of people have to be away from their home and gather in a shelter. In addition, the refugees suffer from the shortage of necessary resources due to impaired life infrastructure, such as damaged roads and communication networks. The degree of reducing damage depends on the amount of food, water, daily necessities and communication resources required by each shelter. How to effectively and efficiently allocate resources according to grasp the exact need of a disaster situation will be an important issue. We estimate the degree of the disaster by collecting and analyzing big data from the SNS, and building a platform for the communication resources to be efficiently and effectively allocated. In order to achieve this goal, we are challenging the following issues A) Understanding situations (user requirements) after disaster occur The SNS streams large scale semantic information about real time situation in society, especially during and after disaster. It is both domain-specific and computational challenge in processing the heterogeneous large data set to extract the exact situational content with reduced semantic uncertainty. The machine learning (ML) and natural language processing (NPL) tool kits are useful in semantic analysis, but still needs domain-specific implementation and computational improvement for the situation understanding from the SNS big data. B) Understanding distribution patterns of situations/users' requirements The disaster related situation is spatiotemporally correlated, and varies dynamically in space and time. It is also domain-specific and computational challenge in estimating the spatiotemporal distribution patterns of the disaster affect based on the spatial big data from SNS. The scan statistics such as the spatial scan have provided well tested mathematical tools and software for spatial data mining. However, new methodologies are necessary since the assumptions have to be different when it meets the spatial big data in SNS. And the computational complexity in spatial big data is also a bottleneck for real-time processing. C) Solving uncertainty of big crowd data One of the major features in big crowd data, e.g., SNS data, is uncertainty behind the data. Especially in a disaster scenario, the collecting time period cannot be long enough to smooth the data automatically. How to efficiently solve uncertainty problem in the big crowd data in a disaster scenario becomes a new and big challenge for disaster management.

With an estimated one billion smartphones producing over 5 petabytes of data a day, the spatially aware mobile device has become a near ubiquitous presence in daily life. Cogent, excellent research in a variety of fields has explored what the spatial data these devices produce can reveal of society, such as analysis of Foursquare check-ins to reveal patterns of mobility for groups through a city. In such studies, the individual intentions, motivations, and desires behind the production of said data can become lost through computational aggregation and analysis. In this commentary, I argue for a rethinking of the epistemological leap from individual to data point through a (re)seating of the reflexive, self-eliciting subject as an object for spatial big data research. To do so, I first situate current research on spatial big data within a computational turn in social sciences that relies overly on the data produced as a stand-in for the subject producing said data. Second, I argue that a recent shift within geography and cognate disciplines toward viewing spatial big data as a form of spatial media allows for study of the sociotechnical processes that produce modern assemblages of data and society. As spatial media, the spatial big data created through mobile device use can be understood as the data of everyday life and as part of the sociotechnical processes that produce individuals, data, and space. Ultimately, to understand the data of everyday life, researchers must write thick descriptions of the stories we tell ourselves about the data we give off to others.

Recent years have seen the emergence of many new and valuable spatial datasets such as trajectories of cell-phones and Global Positioning System (GPS) devices, vehicle engine measurements, global climate models simulation data, volunteered geographic information (VGI), geo-social media, and tweets. The value of these datasets is already evident through many societal applications including disaster management and disease outbreak prediction. However, these location-aware datasets are of a volume, variety, and velocity that exceed the capability of current CyberGIS technologies. We refer to these datasets as Spatial Big Data. In this chapter, we define spatial big data in terms of its value proposition and user experience which depends on the computational platform, use-case, and dataset at hand. We compare spatial big data with traditional spatial data and with other types of big data. We then provide an overview of the current efforts, challenges and opportunities available when spatial big data is enabled via next-generation CyberGIS. Our discussion includes current accomplishments and opportunities from both an analytics and an infrastructure perspective.

In this study, define the concept of spatial big data and special feature of spatial big data, examine information visualization methodology for increase the insight into the data. Also presented problems and solutions in the visualization process. Spatial big data is defined as a result of quantitative expansion from spatial information and qualitative expansion from big data. Characteristics of spatial big data id defined as 6V (Volume, Variety, Velocity, Value, Veracity, Visualization), As the utilization and service aspects of spatial big data at issue, visualization of spatial big data has received attention for provide insight into the spatial big data to improve the data value. Methods of information visualization is organized in a variety of ways through Matthias, Ben, information design textbook, etc, but visualization of the spatial big data will go through the process of organizing data in the target because of the vast amounts of raw data, need to extract information from data for want delivered to user. The extracted information is used efficient visual representation of the characteristic, The large amounts of data representing visually can not provide accurate information to user, need to data reduction methods such as filtering, sampling, data binning, clustering.

Comparing Global Spatial Data on Deforestation for Institutional Analysis in Africa

The use of "Big Data" to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL framework should help users of big data spot concerns in their own work and that of others who rely on such data to draw conclusions with actionable public policy or business implications. To demonstrate, we provide several case studies that show a healthy dose of skepticism can be warranted when working with and interpreting these new big data sources.

This paper comprehensively explores the challenges and opportunities in Information Technology management during the era of big data. It begins by delving into the definition, characteristics, and sources of big data, highlighting its paramount significance for enterprises and organizations. Addressing issues such as data security, privacy protection, processing capacity bottlenecks, and data quality and trustworthiness, effective strategies are proposed. Recommendations include establishing a robust data governance system, leveraging real-time computing, and employing efficient analytical tools, emphasizing reinforced data security measures and enhanced employee training. For tackling data quality and trustworthiness concerns, the paper underscores the importance of establishing a comprehensive data quality management system. Furthermore, a detailed examination of the application of big data in predictive analysis and decision support is provided, along with suggestions to foster a data-driven decision-making culture and enhance employee data analytics skills. In looking ahead, the paper encourages continuous attention to technological innovation, advocating adjustments in IT management strategies to align with the evolving demands of the big data era.

In the era of big data, the platformization and openness of teaching resources, the diversification and innovation of teaching modes, and the ubiquity and initiative of teaching subjects will inevitably have an epoch-making impact on teaching, thereout call for the profound reform of traditional teaching by big data. Combing with the analysis of the basic characteristics of teaching in the era of big data, it is necessary to reflect on many practical problems of traditional teaching, actively explore the practice path of teaching reform in the era of big data and advocate teaching reform from the aspects of teaching concept, role awareness, teaching management, teaching methods and teaching assessment and so on in order to better adapt to the basic requirements of teaching in the era of big data.

Data source selection for information integration in big data era

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

With the improvement of the material standard of living, the demand of the people for spiritual culture continues to increase. In terms of tourism, people have gradually shifted from simple tourism needs to integrated tourism needs. Tourism has become an effective way for people to expand their horizons and enrich their spiritual world. Tourism is one of the first industries to apply network technology. After long‐term exploration and innovation, tourism e‐commerce has developed rapidly. Coupled with the advent of the era of big data (EBD), the concept of customized tourism has gradually entered people’s lives. This paper mainly introduces the research on the impact of e‐commerce tourism on custom tourism in the EBD and intends to provide some ideas and directions for the good development of custom tourism. This paper proposes a research strategy on the impact of tourism e‐commerce on customized tourism in the EBD, including related theoretical research methods, random forest algorithms, support vector machine classification algorithms, and Bayesian estimation algorithms, which are used to customize tourism e‐commerce in the EBD, and research experiment on the impact of tourism. The experimental results show that 79.84% of customers are willing to purchase related products again after experiencing travel customization services using big data technology. By using the various characteristics of tourism big data for data mining and classification, it provides users with personalized travel search services. At the same time, big data technology can provide basic technical support for customized tourism development, which shows that it can also provide customized services for users.