The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona
Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly re...
Ausführliche Beschreibung
Autor*in: |
Simon Elias Bibri [verfasserIn] John Krogstie [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020 |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
In: Energy Informatics - SpringerOpen, 2018, 3(2020), 1, Seite 42 |
---|---|
Übergeordnetes Werk: |
volume:3 ; year:2020 ; number:1 ; pages:42 |
Links: |
---|
DOI / URN: |
10.1186/s42162-020-00108-6 |
---|
Katalog-ID: |
DOAJ04078763X |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ04078763X | ||
003 | DE-627 | ||
005 | 20230308041606.0 | ||
007 | cr uuu---uuuuu | ||
008 | 230227s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1186/s42162-020-00108-6 |2 doi | |
035 | |a (DE-627)DOAJ04078763X | ||
035 | |a (DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a HD9502-9502.5 | |
100 | 0 | |a Simon Elias Bibri |e verfasserin |4 aut | |
245 | 1 | 4 | |a The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona |
264 | 1 | |c 2020 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. | ||
650 | 4 | |a Data-driven cities | |
650 | 4 | |a Smart cities | |
650 | 4 | |a Data-driven sustainable smart cities | |
650 | 4 | |a Data-driven technologies | |
650 | 4 | |a Applied solutions | |
650 | 4 | |a Competences | |
653 | 0 | |a Energy industries. Energy policy. Fuel trade | |
700 | 0 | |a John Krogstie |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t Energy Informatics |d SpringerOpen, 2018 |g 3(2020), 1, Seite 42 |w (DE-627)1031001387 |x 25208942 |7 nnns |
773 | 1 | 8 | |g volume:3 |g year:2020 |g number:1 |g pages:42 |
856 | 4 | 0 | |u https://doi.org/10.1186/s42162-020-00108-6 |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e |z kostenfrei |
856 | 4 | 0 | |u http://link.springer.com/article/10.1186/s42162-020-00108-6 |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2520-8942 |y Journal toc |z kostenfrei |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_DOAJ | ||
912 | |a GBV_ILN_11 | ||
912 | |a GBV_ILN_20 | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_23 | ||
912 | |a GBV_ILN_24 | ||
912 | |a GBV_ILN_31 | ||
912 | |a GBV_ILN_39 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_60 | ||
912 | |a GBV_ILN_62 | ||
912 | |a GBV_ILN_63 | ||
912 | |a GBV_ILN_65 | ||
912 | |a GBV_ILN_69 | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_73 | ||
912 | |a GBV_ILN_95 | ||
912 | |a GBV_ILN_105 | ||
912 | |a GBV_ILN_110 | ||
912 | |a GBV_ILN_151 | ||
912 | |a GBV_ILN_161 | ||
912 | |a GBV_ILN_170 | ||
912 | |a GBV_ILN_213 | ||
912 | |a GBV_ILN_230 | ||
912 | |a GBV_ILN_285 | ||
912 | |a GBV_ILN_293 | ||
912 | |a GBV_ILN_370 | ||
912 | |a GBV_ILN_602 | ||
912 | |a GBV_ILN_2014 | ||
912 | |a GBV_ILN_2055 | ||
912 | |a GBV_ILN_4012 | ||
912 | |a GBV_ILN_4037 | ||
912 | |a GBV_ILN_4112 | ||
912 | |a GBV_ILN_4125 | ||
912 | |a GBV_ILN_4126 | ||
912 | |a GBV_ILN_4249 | ||
912 | |a GBV_ILN_4305 | ||
912 | |a GBV_ILN_4306 | ||
912 | |a GBV_ILN_4307 | ||
912 | |a GBV_ILN_4313 | ||
912 | |a GBV_ILN_4322 | ||
912 | |a GBV_ILN_4323 | ||
912 | |a GBV_ILN_4324 | ||
912 | |a GBV_ILN_4325 | ||
912 | |a GBV_ILN_4335 | ||
912 | |a GBV_ILN_4338 | ||
912 | |a GBV_ILN_4367 | ||
912 | |a GBV_ILN_4700 | ||
951 | |a AR | ||
952 | |d 3 |j 2020 |e 1 |h 42 |
author_variant |
s e b seb j k jk |
---|---|
matchkey_str |
article:25208942:2020----::heegndtdiesatiynisnoaieplesltosossanblt |
hierarchy_sort_str |
2020 |
callnumber-subject-code |
HD |
publishDate |
2020 |
allfields |
10.1186/s42162-020-00108-6 doi (DE-627)DOAJ04078763X (DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e DE-627 ger DE-627 rakwb eng HD9502-9502.5 Simon Elias Bibri verfasserin aut The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. Data-driven cities Smart cities Data-driven sustainable smart cities Data-driven technologies Applied solutions Competences Energy industries. Energy policy. Fuel trade John Krogstie verfasserin aut In Energy Informatics SpringerOpen, 2018 3(2020), 1, Seite 42 (DE-627)1031001387 25208942 nnns volume:3 year:2020 number:1 pages:42 https://doi.org/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e kostenfrei http://link.springer.com/article/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/toc/2520-8942 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020 1 42 |
spelling |
10.1186/s42162-020-00108-6 doi (DE-627)DOAJ04078763X (DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e DE-627 ger DE-627 rakwb eng HD9502-9502.5 Simon Elias Bibri verfasserin aut The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. Data-driven cities Smart cities Data-driven sustainable smart cities Data-driven technologies Applied solutions Competences Energy industries. Energy policy. Fuel trade John Krogstie verfasserin aut In Energy Informatics SpringerOpen, 2018 3(2020), 1, Seite 42 (DE-627)1031001387 25208942 nnns volume:3 year:2020 number:1 pages:42 https://doi.org/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e kostenfrei http://link.springer.com/article/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/toc/2520-8942 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020 1 42 |
allfields_unstemmed |
10.1186/s42162-020-00108-6 doi (DE-627)DOAJ04078763X (DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e DE-627 ger DE-627 rakwb eng HD9502-9502.5 Simon Elias Bibri verfasserin aut The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. Data-driven cities Smart cities Data-driven sustainable smart cities Data-driven technologies Applied solutions Competences Energy industries. Energy policy. Fuel trade John Krogstie verfasserin aut In Energy Informatics SpringerOpen, 2018 3(2020), 1, Seite 42 (DE-627)1031001387 25208942 nnns volume:3 year:2020 number:1 pages:42 https://doi.org/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e kostenfrei http://link.springer.com/article/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/toc/2520-8942 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020 1 42 |
allfieldsGer |
10.1186/s42162-020-00108-6 doi (DE-627)DOAJ04078763X (DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e DE-627 ger DE-627 rakwb eng HD9502-9502.5 Simon Elias Bibri verfasserin aut The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. Data-driven cities Smart cities Data-driven sustainable smart cities Data-driven technologies Applied solutions Competences Energy industries. Energy policy. Fuel trade John Krogstie verfasserin aut In Energy Informatics SpringerOpen, 2018 3(2020), 1, Seite 42 (DE-627)1031001387 25208942 nnns volume:3 year:2020 number:1 pages:42 https://doi.org/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e kostenfrei http://link.springer.com/article/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/toc/2520-8942 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020 1 42 |
allfieldsSound |
10.1186/s42162-020-00108-6 doi (DE-627)DOAJ04078763X (DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e DE-627 ger DE-627 rakwb eng HD9502-9502.5 Simon Elias Bibri verfasserin aut The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. Data-driven cities Smart cities Data-driven sustainable smart cities Data-driven technologies Applied solutions Competences Energy industries. Energy policy. Fuel trade John Krogstie verfasserin aut In Energy Informatics SpringerOpen, 2018 3(2020), 1, Seite 42 (DE-627)1031001387 25208942 nnns volume:3 year:2020 number:1 pages:42 https://doi.org/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e kostenfrei http://link.springer.com/article/10.1186/s42162-020-00108-6 kostenfrei https://doaj.org/toc/2520-8942 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2020 1 42 |
language |
English |
source |
In Energy Informatics 3(2020), 1, Seite 42 volume:3 year:2020 number:1 pages:42 |
sourceStr |
In Energy Informatics 3(2020), 1, Seite 42 volume:3 year:2020 number:1 pages:42 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Data-driven cities Smart cities Data-driven sustainable smart cities Data-driven technologies Applied solutions Competences Energy industries. Energy policy. Fuel trade |
isfreeaccess_bool |
true |
container_title |
Energy Informatics |
authorswithroles_txt_mv |
Simon Elias Bibri @@aut@@ John Krogstie @@aut@@ |
publishDateDaySort_date |
2020-01-01T00:00:00Z |
hierarchy_top_id |
1031001387 |
id |
DOAJ04078763X |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ04078763X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308041606.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s42162-020-00108-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ04078763X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HD9502-9502.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Simon Elias Bibri</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-driven cities</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Smart cities</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-driven sustainable smart cities</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-driven technologies</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Applied solutions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Competences</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Energy industries. Energy policy. Fuel trade</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">John Krogstie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Energy Informatics</subfield><subfield code="d">SpringerOpen, 2018</subfield><subfield code="g">3(2020), 1, Seite 42</subfield><subfield code="w">(DE-627)1031001387</subfield><subfield code="x">25208942</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:42</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s42162-020-00108-6</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://link.springer.com/article/10.1186/s42162-020-00108-6</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2520-8942</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="h">42</subfield></datafield></record></collection>
|
callnumber-first |
H - Social Science |
author |
Simon Elias Bibri |
spellingShingle |
Simon Elias Bibri misc HD9502-9502.5 misc Data-driven cities misc Smart cities misc Data-driven sustainable smart cities misc Data-driven technologies misc Applied solutions misc Competences misc Energy industries. Energy policy. Fuel trade The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona |
authorStr |
Simon Elias Bibri |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)1031001387 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
HD9502-9502 |
illustrated |
Not Illustrated |
issn |
25208942 |
topic_title |
HD9502-9502.5 The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona Data-driven cities Smart cities Data-driven sustainable smart cities Data-driven technologies Applied solutions Competences |
topic |
misc HD9502-9502.5 misc Data-driven cities misc Smart cities misc Data-driven sustainable smart cities misc Data-driven technologies misc Applied solutions misc Competences misc Energy industries. Energy policy. Fuel trade |
topic_unstemmed |
misc HD9502-9502.5 misc Data-driven cities misc Smart cities misc Data-driven sustainable smart cities misc Data-driven technologies misc Applied solutions misc Competences misc Energy industries. Energy policy. Fuel trade |
topic_browse |
misc HD9502-9502.5 misc Data-driven cities misc Smart cities misc Data-driven sustainable smart cities misc Data-driven technologies misc Applied solutions misc Competences misc Energy industries. Energy policy. Fuel trade |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
cr |
hierarchy_parent_title |
Energy Informatics |
hierarchy_parent_id |
1031001387 |
hierarchy_top_title |
Energy Informatics |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)1031001387 |
title |
The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona |
ctrlnum |
(DE-627)DOAJ04078763X (DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e |
title_full |
The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona |
author_sort |
Simon Elias Bibri |
journal |
Energy Informatics |
journalStr |
Energy Informatics |
callnumber-first-code |
H |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
txt |
container_start_page |
42 |
author_browse |
Simon Elias Bibri John Krogstie |
container_volume |
3 |
class |
HD9502-9502.5 |
format_se |
Elektronische Aufsätze |
author-letter |
Simon Elias Bibri |
doi_str_mv |
10.1186/s42162-020-00108-6 |
author2-role |
verfasserin |
title_sort |
emerging data–driven smart city and its innovative applied solutions for sustainability: the cases of london and barcelona |
callnumber |
HD9502-9502.5 |
title_auth |
The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona |
abstract |
Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. |
abstractGer |
Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. |
abstract_unstemmed |
Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
container_issue |
1 |
title_short |
The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona |
url |
https://doi.org/10.1186/s42162-020-00108-6 https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e http://link.springer.com/article/10.1186/s42162-020-00108-6 https://doaj.org/toc/2520-8942 |
remote_bool |
true |
author2 |
John Krogstie |
author2Str |
John Krogstie |
ppnlink |
1031001387 |
callnumber-subject |
HD - Industries, Land Use, Labor |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1186/s42162-020-00108-6 |
callnumber-a |
HD9502-9502.5 |
up_date |
2024-07-03T16:45:15.407Z |
_version_ |
1803577055413010432 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ04078763X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230308041606.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230227s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s42162-020-00108-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ04078763X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ9bd1834d432e4fd6bb9a8771ae050c3e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HD9502-9502.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Simon Elias Bibri</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The emerging data–driven Smart City and its innovative applied solutions for sustainability: the cases of London and Barcelona</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The big data revolution is heralding an era where instrumentation, datafication, and computation are increasingly pervading the very fabric of cities. Big data technologies have become essential to the functioning of cities. Consequently, urban processes and practices are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. Such form is increasingly being directed towards tackling the challenges of sustainability in the light of the escalating urbanization trend. This paper investigates how the emerging data-driven smart city is being practiced and justified in terms of the development and implementation of its innovative applied solutions for sustainability. To illuminate this new urban phenomenon, a descriptive case study is adopted as a qualitative research methodology to examine and compare London and Barcelona as the leading data-driven smart cities in Europe. This study shows that these cities have a high level of the development of applied data-driven technologies, but they slightly differ in the level of the implementation of such technologies in different city systems and domains with respect to sustainability areas. They also moderately differ in the degree of their readiness as to the availability and development level of the competences and infrastructure needed to generate, transmit, process, and analyze large masses of data to extract useful knowledge for enhanced decision making and deep insights pertaining to urban operational functioning, management, and planning in relation to sustainability. London takes the lead as regards the ICT infrastructure and data sources, whereas Barcelona has the best practices in the data-oriented competences, notably horizontal information platforms, operations centers, dashboards, training programs and educational institutes, innovation labs, research centers, and strategic planning offices. This research enhances the scholarly community’s current understanding of the new phenomenon of the data-driven city with respect to the untapped synergic potential of the integration of smart urbanism and sustainable urbanism for advancing sustainability in the light of the emerging paradigm of big data computing. No previous work has, to the best of our knowledge, explored and highlighted the link between the data-driven smart solutions and the sustainable development strategies in the context of data-driven sustainable smart cities as a new paradigm of urbanism.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-driven cities</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Smart cities</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-driven sustainable smart cities</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data-driven technologies</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Applied solutions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Competences</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Energy industries. Energy policy. Fuel trade</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">John Krogstie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Energy Informatics</subfield><subfield code="d">SpringerOpen, 2018</subfield><subfield code="g">3(2020), 1, Seite 42</subfield><subfield code="w">(DE-627)1031001387</subfield><subfield code="x">25208942</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:42</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1186/s42162-020-00108-6</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/9bd1834d432e4fd6bb9a8771ae050c3e</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://link.springer.com/article/10.1186/s42162-020-00108-6</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2520-8942</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="h">42</subfield></datafield></record></collection>
|
score |
7.401124 |