scholarly journals Deteção remota

2021 ◽  
Vol 9 (1) ◽  
pp. e00155
Author(s):  
Catarina de Almeida Pinheiro
Keyword(s):  
Big Data ◽  
Bottom Up

A parca existência de dados espaciotemporais contínuos desde sempre impôs consideráveis restrições a abordagens transversais e diacrónicas do fenómeno urbano. Todavia, avanços tecnológicos, mais ou menos recentes, que diariamente incrementam colossais bases de dados com informação geográfica explícita (e.g., satélites, redes sociais, dados oficiais), vieram possibilitar a desmultiplicação dos estudos de caso. Com efeito, a lógica dedutiva, hegemónica até então, é substituída pela indutiva – que desenvolvida no sentido bottom-up veio colocar em evidência particularismos locais. Nascida ainda antes do apogeu dos ‘Big Data’, a observação sinótica e repetitiva da Terra facultada pela Deteção Remota assume uma importância fulcral no (re)interesse e na renovação metodológica e concetual dos Estudos Urbanos verificada no advento do novo milénio – mormente o enfoque cronogeográfico que é dado aos estudos de morfologia urbana. A par disto, importa notar que a visão multiespectral dos satélites fornece variegada informação (e.g., humidade do solo, temperatura de superfície, poluentes), que se estende muito para além da mera extração do tecido urbano. Face ao exposto, procura-se colocar em evidência as mutações que a Deteção Remota – alicerçada nos Sistemas de Informação Geográfica – desencadeou nos Estudos Urbanos, dando-se particular enfoque ao domínio da Geografia, visto aí a abordagem integrada do ecossistema urbano se encontrar maximizada.

scholarly journals Top-Down versus Bottom-Up Theories of Phonological Acquisition: A Big Data Approach

2017 ◽  
Author(s):  
Christina Bergmann ◽  
Sho Tsuji ◽  
Alejandrina Cristia
Keyword(s):  
Big Data ◽  
Phonological Acquisition ◽  
Top Down ◽  
Bottom Up

scholarly journals Human-in-the-loop energy flexibility integration on a neighbourhood level: Small and Big Data management

2019 ◽  
Vol 40 (3) ◽  
pp. 305-318 ◽  
Author(s):  
Wim Zeiler ◽  
Timi Labeodan
Keyword(s):  
Big Data ◽  
System Integration ◽  
Energy System ◽  
Office Building ◽  
Small Data ◽  
Bottom Up ◽  
Human In The Loop ◽  
Starting Point ◽  
Neighbourhood Level ◽  
Operational Data

Modern buildings provide an enormous amount of data available from various sources ranging from modular wireless sensors to smart meters. As well as enhancing energy management and building performance, the analysis of these datasets can enhance the management of decentralized energy systems (electrical storage, PV generation, heat storage, etc.). To optimize the interaction between the building and the grid, it is essential to determine the total energy flexibility of the user and the building. A building has different possibilities for demand side management, energy storage and energy exchange for which a functional-layered approach is proposed from the user up to building and its interaction with the energy infrastructure. Central is the principle of the human-in-the-loop, where a bottom-up approach places the human needs as a central starting point for the energy interaction optimisation. The combination of Big Data with deep learning techniques offers new possibilities in the prediction of energy use and decentralized renewable energy production (e.g. from local weather data taking into account local phenomena such as urban heat islands). This combined with a more bottom-up approach of multi-agent systems with a gossip-based cooperative approach using Small Data offers decentralized control and monitoring autonomy to reduce the complexity of the energy system integration and transition. This makes it possible to relate the outcomes of the urban energy system integration on a neighbourhood level. The approach is being applied to a typically medium-sized office building. A first application of the human-in-the-loop controlling the lighting systems in the open-plan workplace of the test-bed office building showed some estimated annual energy saving of around 24%. Practical application: Analysis of a large database containing so called Big Data of clusters of buildings seems promising. Therefor there is the need to study the potential impact of utilization of big building operational data in building services industry. Besides this there is also a need for a data mining-based method for analyzing massive building operational data of a specific building, Small Data. This work sets out a general framework and method for doing both and to combine the strength of both approaches. The presented combined approach and results will be of interest to engineers and facility managers wondering what the key constraints to optimal use data to optimize low energy/carbon control strategies might have within their work.

scholarly journals From Petty Corruption to Criminal State

Intersections ◽  
2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Bálint Magyar ◽  
Bálint Madlovics
Keyword(s):  
Big Data ◽  
Free Market ◽  
Analytical Framework ◽  
The State ◽  
Top Down ◽  
Bottom Up ◽  
Country Comparison ◽  
State Capture ◽  
Cross Country ◽  
Four Levels

Offering a decent database easily applicable to cross-country comparison, Transparency International’s Corruption Perceptions Index (CPI) has been widely used as a variable for showing the level of corruption. However, surveys of its sources are based on presumptions which mainly apply to bottom-up forms of corruption, namely free market corruption and bottom-up state capture, and therefore it is insufficient for assessing the state of a country plagued by top-down types of the former. We provide an analytical framework that distinguishes four levels of corruption and draws on the experience of the post-communist region. Using this framework to analyze the CPI’s survey questions, we explain why the index provides a blurred picture of the region. ‘Big data’ evidence for top-down corruption in Hungary is also presented, signifying the need for a more refined index.

“Bio to bits”: the Millennium Ecosystem Assessment (MA) as a metaphor for Big Data ecosystem assessment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sepehr Ghazinoory ◽  
Shohreh Nasri ◽  
Roya Dastranj ◽  
Alfred Sarkissian
Keyword(s):  
Big Data ◽  
User Needs ◽  
Millennium Ecosystem Assessment ◽  
End User ◽  
Drivers Of Change ◽  
Top Down ◽  
Bottom Up ◽  
Content Type ◽  
Ecosystem Assessment ◽  
Data Ecosystem

PurposeBig Data (BD) is not only a quintessential part of many economic activities but also has evolved into a veritable business ecosystem. However, most Big Data ecosystem (BDE) models have a technical, bottom-up focus and mostly lack the capability for a broad socioeconomic analysis. This paper identifies the Millennium Ecosystem Assessment (MA) as a useful, operational framework and uses a metaphorical analogy to adapt it for the BDE. The top-down approach adopted here allows for seeing the big picture of the BD ecosystem. Meeting “end-user needs” is the main objective of the proposed BDE framework.Design/methodology/approachThe methodology of this paper consists of two parts. First, the MA is adapted for the BDE through a metaphorical analogy. Then, to operationalize and validate the proposed framework, it is applied to an emerging BD ecosystem.FindingsIn total, four types of services are offered in the BD ecosystem: provisioning information and products; regulating; cultural and supporting services. Direct and indirect drivers of change impact ecosystem processes such as BD service provision. Based on the assessment results, interventions can be devised to remedy problems, sustain the ecosystem or accelerate growth. The proposed BDE assessment framework is applied to an emerging BDE as an example of operationalization and validation of the proposed BDE framework.Originality/valueThe strengths of the proposed BDE framework is that, in contrast to existing frameworks that are technical and bottom-up, it is constructed top-down by a metaphorical analogy from the proven MA framework. It is a generic framework with the ultimate objective of meeting the “end-user needs” and does not focus on a single sector or firm. Also, the proposed BDE framework is multi-faceted and considers broad socioeconomic issues such as regulating, cultural and supporting services and drivers of change.

scholarly journals Big Data from the bottom up

2014 ◽  
Vol 1 (2) ◽  
pp. 205395171453927 ◽  
Author(s):  
Nick Couldry ◽  
Alison Powell
Keyword(s):  
Big Data ◽  
Bottom Up

An Advanced Bottom up Generalization Approach for Big Data on Cloud

2014 ◽  
Vol 003 (001) ◽  
pp. 12-15
Author(s):  
K.R. Pandilakshmi ◽  
◽  
G.Rashitha Banu ◽  
Keyword(s):  
Big Data ◽  
Bottom Up

scholarly journals POTENTIAL OF BIG DATA FOR PRO-ACTIVE PARTICIPATORY LAND USE PLANNING

2018 ◽  
Vol 5 (2) ◽  
pp. 205 ◽  
Author(s):  
Walter Timo De Vries
Keyword(s):  
Land Use ◽  
Big Data ◽  
Citizen Participation ◽  
Land Use Planning ◽  
Full Potential ◽  
Bottom Up ◽  
Type Of Land Use ◽  
Spatial Big Data ◽  
Hierarchical Governance ◽  
Analyse Data

The presence of (spatial) big data presumes that citizens can more actively collect and analyse data for their own land use goals. This article evaluates that claim. Given that land use planning heavily depends on participation and citizens’ own contributions the core question is whether and how (spatial) big data can enhance and/or complement current land use planning endeavours. The article starts by defining and conceptualising the various phases and objectives of land use planning. This is needed to verify where citizen participation can play a crucial role and where bottom-up influence can actually emerge.  The article is fundamentally explorative. It relies on evaluating existing websites and documentation which conceptualise (spatial) big data and smart application, with a particular emphasis on ‘smart people’. A number of specific cases are explored in order to verify how and in which type of land use planning activity citizens are actively.  The evaluation indicates that many the smart applications making use of big data are still largely driven by conventional hierarchical governance structures. The choice of data and associated analytics are still largely confined and the opportunities whereby the designs of the new and alternative land use options by citizens are accepted or adopted is still limited. The take-home message is that adoption of big data for the purpose of empowering citizens is still limited. There probably needs to be more exemplary projects and various forms of capacity development and exploratory pilots before the full potential of (spatial) big data can be employed for bottom-up land use planning.

scholarly journals The hierarchy-of-hypotheses approach: A synthesis method for enhancing theory development in ecology and evolution

2019 ◽  
Author(s):  
Tina Heger ◽  
Carlos Aguilar ◽  
Isabelle Bartram ◽  
Raul Rennó Braga ◽  
Gregory P. Dietl ◽  
...  
Keyword(s):  
Big Data ◽  
Structure Data ◽  
Evolutionary Biology ◽  
Meta Analysis ◽  
Synthesis Method ◽  
Theory Development ◽  
Top Down ◽  
Bottom Up ◽  
Empirical Results ◽  
Develop Theory

In the current era of Big Data, existing synthesis tools (e.g. formal meta-analysis) are useful for handling the deluge of data and information. However, there is a need for complementary tools that help to (i) structure data and information, (ii) closely connect evidence to theory and (iii) further develop theory. We present the hierarchy-of-hypotheses (HoH) approach to address these issues. In an HoH, hypotheses are conceptually and visually structured in a hierarchically nested way, where the lower branches can be directly connected to empirical results. Used as an evidence-driven, bottom-up approach, it can (i) show connections between empirical results, even when derived through diverse approaches; and (ii) indicate under which circumstances hypotheses are applicable. Used as a theory-driven, top-down method, it helps uncover mechanistic components of hypotheses. We offer guidance on how to build an HoH, provide examples from population and evolutionary biology and propose terminological clarifications.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

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