Ontology of core concept data types for answering geo-analytical questions

In geographic information systems (GIS), analysts answer questions by designing workflows that transform a certain type of data into a certain type of goal. Semantic data types help constrain the application of computational methods to those that are meaningful for such a goal. This prevents pointless computations and helps analysts design effective workflows. Yet, to date it remains unclear which types would be needed in order to ease geo-analytical tasks. The data types and formats used in GIS still allow for huge amounts of syntactically possible but nonsensical method applications. Core concepts of spatial information and related geo-semantic distinctions have been proposed as abstractions to help analysts formulate analytic questions and to compute appropriate answers over geodata of different formats. In essence, core concepts reflect particular interpretations of data which imply that certain transformations are possible. However, core concepts usually remain implicit when operating on geodata, since a concept can be represented in a variety of forms. A central question therefore is: Which semantic types would be needed to capture this variety and its implications for geospatial analysis? In this article, we propose an ontology design pattern of core concept data types that help answer geo-analytical questions. Based on a scenario to compute a liveability atlas for Amsterdam, we show that diverse kinds of geo-analytical questions can be answered by this pattern in terms of valid, automatically constructible GIS workflows using standard sources

RANCANG BANGUN APLIKASI PENDATAAN SENSUS EKONOMI BERBASIS MOBILE

Badan Pusat Statistik (BPS) merupakan sebuah institusi pemerintah yang mempunyai hak untuk melakukan kegiatan statistik yaitu berupa sensus dan survei. BPS melakukan pengolahan data dari beberapa sensus, cotohnya yaitu tentang sosial dan kependudukan, ekonomi dan perdagangan, serta pertanian dan pertambangan. Setiap data dan keputusan yang diambil harus berdasarkan informasi yang valid dan akurat. Namun semakin meningkatnya permintaan data dan informasi maka akan berpengaruh pula pada meningkatnya kegiatan survei yang tidak sebanding dengan terbatasnya jumlah SDM. Meski BPS mempunyai sistem dan infrastruktur teknik informasi yang memadai, namun masih ada sistem yang belum sepenuhnya terintegrasi dengan baik. Sistem yang akan dibuat adalah sistem pada aplikasi yang digunakan BPS dalam pengolahan data sensus ekonomi untuk Usaha Mikro dan Kecil. Sistem tersebut dianalisis dengan cara pengumpulan data dengan menggunakan kuesioner. Sistem yang akan dianalisis tersebut diharapkan dapat dibuat aplikasi dengan menggunakan model Waterfall.Dengan menggunakan model Waterfall maka kualitas pengembangan yang dihasilkan akan lebih baik karena dilakukan secara bertahap sehingga sistem yang akan dikembangkan dapat membantu petugas dalam mengolah data.Badan Pusat Statistik (BPS) merupakan sebuah institusi pemerintah yang mempunyai hak untuk melakukan kegiatan statistik yaitu berupa sensus dan survei. BPS melakukan pengolahan data dari beberapa sensus, cotohnya yaitu tentang sosial dan kependudukan, ekonomi dan perdagangan, serta pertanian dan pertambangan. Setiap data dan keputusan yang diambil harus berdasarkan informasi yang valid dan akurat. Namun semakin meningkatnya permintaan data dan informasi maka akan berpengaruh pula pada meningkatnya kegiatan survei yang tidak sebanding dengan terbatasnya jumlah SDM. Meski BPS mempunyai sistem dan infrastruktur teknik informasi yang memadai, namun masih ada sistem yang belum sepenuhnya terintegrasi dengan baik. Sistem yang akan dibuat adalah sistem pada aplikasi yang digunakan BPS dalam pengolahan data sensus ekonomi untuk Usaha Mikro dan Kecil. Sistem tersebut dianalisis dengan cara pengumpulan data dengan menggunakan kuesioner. Sistem yang akan dianalisis tersebut diharapkan dapat dibuat aplikasi dengan menggunakan model Waterfall.Dengan menggunakan model Waterfall maka kualitas pengembangan yang dihasilkan akan lebih baik karena dilakukan secara bertahap sehingga sistem yang akan dikembangkan dapat membantu petugas dalam mengolah data

Reproducible research and GIScience: an evaluation using AGILE conference papers

The demand for reproducible research is on the rise in disciplines concerned with data analysis and computational methods. Therefore, we reviewed current recommendations for reproducible research and translated them into criteria for assessing the reproducibility of articles in the field of geographic information science (GIScience). Using this criteria, we assessed a sample of GIScience studies from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, and we collected feedback about the assessment from the study authors. Results from the author feedback indicate that although authors support the concept of performing reproducible research, the incentives for doing this in practice are too small. Therefore, we propose concrete actions for individual researchers and the GIScience conference series to improve transparency and reproducibility. For example, to support researchers in producing reproducible work, the GIScience conference series could offer awards and paper badges, provide author guidelines for computational research, and publish articles in Open Access formats

Design and semantics of form and movement (DeSForM 2006)

Design and Semantics of Form and Movement (DeSForM) grew from applied research exploring emerging design methods and practices to support new generation product and interface design. The products and interfaces are concerned with: the context of ubiquitous computing and ambient technologies and the need for greater empathy in the pre-programmed behaviour of the ‘machines’ that populate our lives. Such explorative research in the CfDR has been led by Young, supported by Kyffin, Visiting Professor from Philips Design and sponsored by Philips Design over a period of four years (research funding £87k). DeSForM1 was the first of a series of three conferences that enable the presentation and debate of international work within this field: • 1st European conference on Design and Semantics of Form and Movement (DeSForM1), Baltic, Gateshead, 2005, Feijs L., Kyffin S. & Young R.A. eds. • 2nd European conference on Design and Semantics of Form and Movement (DeSForM2), Evoluon, Eindhoven, 2006, Feijs L., Kyffin S. & Young R.A. eds. • 3rd European conference on Design and Semantics of Form and Movement (DeSForM3), New Design School Building, Newcastle, 2007, Feijs L., Kyffin S. & Young R.A. eds. Philips sponsorship of practice-based enquiry led to research by three teams of research students over three years and on-going sponsorship of research through the Northumbria University Design and Innovation Laboratory (nuDIL). Young has been invited on the steering panel of the UK Thinking Digital Conference concerning the latest developments in digital and media technologies. Informed by this research is the work of PhD student Yukie Nakano who examines new technologies in relation to eco-design textiles

EO + morphometrics : understanding cities through urban morphology at large scale

Earth Observation (EO)-based mapping of cities has great potential to detect patterns beyond the physical ones. However, EO combined with the surge of machine learning techniques to map non-physical, such as socioeconomic, aspects directly, goes to the expense of reproducibility and interpretability, hence scientific validity. In this paper, we suggest shifting the focus from the direct detection of socioeconomic status from raw images through image features, to the mapping of interpretable urban morphology of basic urban elements as an intermediate step, to which socioeconomic patterns can then be related. This shift is profound, in that, rather than abstract image features, it allows to capture the morphology of real urban objects, such as buildings and streets, and use this to then interpret other patterns, including socioeconomic ones. Because socioeconomic patterns are not derived from raw image data, the mapping of these patterns is less data demanding and more replicable. Specifically, we propose a 2-step approach: (1) extraction of fundamental urban elements from satellite imagery, and (2) derivation of meaningful urban morphological patterns from the extracted elements. We refer to this 2-step approach as “EO + Morphometrics”. Technically, EO consists of applying deep learning through a reengineered U-Net shaped convolutional neural network to publicly accessible Google Earth imagery for building extraction. Methods of urban morphometrics are then applied to these buildings to compute semantically explicit and interpretable metrics of urban form. Finally, clustering is applied to these metrics to obtain morphological patterns, or urban types. The “EO + Morphometrics” approach is applied to the city of Nairobi, Kenya, where 15 different urban types are identified. To test whether this outcome meaningfully describes current urbanization patterns, we verified whether selected types matched locally designated informal settlements. We observe that four urban types, characterized by compact and organic urban form, were recurrent in such settlements. The proposed "EO + Morphometrics" approach paves the way for the large-scale identification of interpretable urban form patterns and study of associated dynamics across any region in the world

A KD framework in football data analytics: a value co-creation framework for the use of knowledge discovery technologies in the football industry

Investment in sport technologies are expected to grow by 40.1% during 2016-2022 reaching approximately $3.97 billion by 2022. As well the recent changes in technology regulations by The Federation Internationale de Football Association (FIFA) since the 2018 World Cup created promising football technologies. This research questions addressing the issue of what is the value of such technologies for professional football teams? and what are the benefits of these technologies? This is achieved by developing a framework for understanding the value co-creation process from the knowledge discovery systems in the football industry. The framework aids in mapping the resources, pinpointing the outputs, identifying the competencies leading into capabilities, and finally in realisation of the value of the final outcomes in that journey. On another words, different teams have different resources that allow them to achieve certain outputs. These outputs enable the coaching team to achieve and maintain certain abilities. By changes in practice the will improve the team ability and enhance their analytical capabilities. Therefore, that will allow and aid the coaching team to gain new outcomes such as improving training strategies, transferring players, and informative match strategies. Additionally, improved understanding of the value co-creation process from the knowledge discovery systems in the football industry answering, why are some teams better able to gain value from investment in knowledge discovery technologies than other teams in the football industry. The framework has been developed in three phases in which semi-structured interviews where used in the first and second phases for developing and validating the framework respectively. The third and final phases is verifying the framework by developing a knowledge discovery maturity model as an online assessment s tool in operationalising the research findings. The main contributions of this research are the adaptation and customisation of Melville et al. (2004) to develop a value co-creation process form knowledge discovery resources. Moreover, applying Agile (APM, 2015) artefacts and techniques and tools in improving the value co-creation process between coaches and data analysts. That s aided in developing the value co-creation knowledge discovery framework in football analytics. Additionally, the development of a key performance indicators balanced scorecard and its adaptation as a in understanding the relationships between the key performance indicators (i.e. physical, psychological, technical and tactical performance indicators). Finally, the development of the knowledge discovery maturity model in football analytics which was used in understanding and pinpointing areas of strength and weakness in the utilisation of the various football resources used in football analytics (human resources, technological resources, value co-creation resources and analytical models used)

Why good data analysts need to be critical synthesists : Determining the role of semantics in data analysis

In this article, we critically examine the role of semantic technology in data driven analysis. We explain why learning from data is more than just analyzing data, including also a number of essential synthetic parts that suggest a revision of George Box’s model of data analysis in statistics. We review arguments from statistical learning under uncertainty, workflow reproducibility, as well as from philosophy of science, and propose an alternative, synthetic learning model that takes into account semantic conflicts, observation, biased model and data selection, as well as interpretation into background knowledge. The model highlights and clarifies the different roles that semantic technology may have in fostering reproduction and reuse of data analysis across communities of practice under the conditions of informational uncertainty. We also investigate the role of semantic technology in current analysis and workflow tools, compare it with the requirements of our model, and conclude with a roadmap of 8 challenging research problems which currently seem largely unaddressed

Why good data analysts need to be critical synthesists: Determining the role of semantics in data analysis

In this article, we critically examine the role of semantic technology in data driven analysis. We explain why learning from data is more than just analyzing data, including also a number of essential synthetic parts that suggest a revision of George Box’s model of data analysis in statistics. We review arguments from statistical learning under uncertainty, workflow reproducibility, as well as from philosophy of science, and propose an alternative, synthetic learning model that takes into account semantic conflicts, observation, biased model and data selection, as well as interpretation into background knowledge. The model highlights and clarifies the different roles that semantic technology may have in fostering reproduction and reuse of data analysis across communities of practice under the conditions of informational uncertainty. We also investigate the role of semantic technology in current analysis and workflow tools, compare it with the requirements of our model, and conclude with a roadmap of 8 challenging research problems which currently seem largely unaddressed

Why good data analysts need to be critical synthesists. Determining the role of semantics in data analysis

In this article, we critically examine the role of semantic technology in data driven analysis. We explain why learning from data is more than just analyzing data, including also a number of essential synthetic parts that suggest a revision of George Box's model of data analysis in statistics. We review arguments from statistical learning under uncertainty, workflow reproducibility, as well as from philosophy of science, and propose an alternative, synthetic learning model that takes into account semantic conflicts, observation, biased model and data selection, as well as interpretation into background knowledge. The model highlights and clarifies the different roles that semantic technology may have in fostering reproduction and reuse of data analysis across communities of practice under the conditions of informational uncertainty. We also investigate the role of semantic technology in current analysis and workflow tools, compare it with the requirements of our model, and conclude with a roadmap of 8 challenging research problems which currently seem largely unaddressed

Depression Among Older Immigrant African Women in Metro West Massachusetts

AbstractThis study addressed depression among older immigrant African Women in Metro West Massachusetts to better understand the causes and prevalence of depression among older women coming from Africa to live in the United States. The study situated older women to be above the age of 55 and focused on exploring the views of social workers in relation to issues of depression among these women. It addressed the factors that influence older immigrant African women’s adaptive capacity in Metro West Massachusetts, the mental health service social workers offer to older women from Africa living in the United States, how social workers assess the need for mental health services for their clients, and the service modifications social workers can introduce to enhance mental health service delivery for older women coming from Africa. These issues were investigated from the perspectives of the structural-functional theory, whereby data were collected using interviews. Data were transcribed, coded and theme developed. The sample size was limited to 12 participants who were selected using quota sampling. The findings obtained indicated that socioeconomic and cultural background; reasons for migrating; experiences before, during, and after migration; English language proficiency; stigma and marginalization; residential location; and individual characteristics are some of the factors that influence the adaptive capacity and mental health of older African women immigrants. These findings indicate that for positive social change social workers should focus on understanding these factors to enhance the effectiveness of their mental health service delivery