Atnaujinkite slapukų nuostatas

El. knyga: Spatial Data and Intelligence: 4th International Conference, SpatialDI 2023, Nanchang, China, April 13-15, 2023, Proceedings

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
  • Formatas: PDF+DRM
  • Serija: Lecture Notes in Computer Science 13887
  • Išleidimo metai: 10-May-2023
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783031329104
  • Formatas: PDF+DRM
  • Serija: Lecture Notes in Computer Science 13887
  • Išleidimo metai: 10-May-2023
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783031329104

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

This book constitutes the refereed proceedings of the 4th International Conference on Spatial Data and Intelligence, SpatialDI 2023, held in Nanchang, China, in April 13–15, 2023.

The 18 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: traffic management; visualization analysis; spatial big data analysis; spatiotemporal data mining; spatiotemporal data storage; and metaverse.

Traffic Management.- APADGCN: Adaptive Partial Attention Diffusion Graph
Convolutional Network for Traffic Flow Forecasting.- DeepParking: Deep
Learning-based Planning Method for Autonomous Parking.- Recommendations for
Urban Planning based on Non-motorized Travel Data and Street Comfort.- A
Composite Grid Clustering Algorithm based on Density and Balance
Degree.- Visualization Analysis.- Research on the Visualization Method of
Weibo User Sentiment Analysis based on IP Affiliation and Comment Content.-
Village Web 3D Visualization System based on Cesium.- Spatial Big Data
Analysis.- Spatial-Aware Community Search over Heterogeneous Information
Networks.- Ship Classification Based on Trajectories Data and LightGBM
Considering Offshore Distance Feature.- CDGCN: An Effective and Efficient
Algorithm based on Community Detection for Training Deep and Large Graph
Convolutional Networks.- Investigate the Relationship between Traumatic
Occurrencesand Socio-economic Status based on Geographic Information System
(GIS): The Case of Qingpu in Shanghai, China.- Contact Query Processing based
on Spatiotemporal Trajectory.- Influential Community Search over Large
Heterogeneous Information Networks.- Spatiotemporal Data Mining.- Fast Mining
Prevalent Co-location Patterns over Dense Spatial Datasets.- Continuous
Sub-prevalent Co-location Pattern Mining.- The Abnormal Detection Method of
Ship Trajectory with Adaptive Transformer Model based on Migration Learning.-
Spatiotemporal Data Storage.- A Comparative Study of Row and Column Storage
for Time Series Data.- LOACR: A Cache Replacement Method Based on Loop
Assist.- Metaverse.- Unifying Reality and Virtuality: Constructing a Cohesive
Metaverse Using Complex Numbers.