標(biāo)題: Titlebook: Handbook of Big Geospatial Data; Martin Werner,Yao-Yi Chiang Book 2021 Springer Nature Switzerland AG 2021 Algorithms for Big Data.Geograp [打印本頁] 作者: intrinsic 時間: 2025-3-21 18:38
書目名稱Handbook of Big Geospatial Data影響因子(影響力)
書目名稱Handbook of Big Geospatial Data影響因子(影響力)學(xué)科排名
書目名稱Handbook of Big Geospatial Data網(wǎng)絡(luò)公開度
書目名稱Handbook of Big Geospatial Data網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Handbook of Big Geospatial Data被引頻次
書目名稱Handbook of Big Geospatial Data被引頻次學(xué)科排名
書目名稱Handbook of Big Geospatial Data年度引用
書目名稱Handbook of Big Geospatial Data年度引用學(xué)科排名
書目名稱Handbook of Big Geospatial Data讀者反饋
書目名稱Handbook of Big Geospatial Data讀者反饋學(xué)科排名
作者: 豪華 時間: 2025-3-22 00:00 作者: 碎石頭 時間: 2025-3-22 04:13 作者: HILAR 時間: 2025-3-22 08:34 作者: antipsychotic 時間: 2025-3-22 10:46
Atoms, Small, and Large Molecules,ntaining geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data domain can be summarized as the ‘Big Vs’ – variety, volume, ve作者: 通便 時間: 2025-3-22 16:05
Alhussein Albarbar,Canras Batunlu such as Statistical and Mapping Agencies. Together, these public institutions publish territorial statistics that are of utmost importance for policy-makers to conduct various analyses of their jurisdiction, in time and space, and observe its evolution over time. However, through times, all over th作者: 獸群 時間: 2025-3-22 17:29
Reliability Assessment of TBCs, recent years, the widespread adoption of location-aware technologies such as the GPS-enabled smartphones amass flow data at individual level, along with much finer spatiotemporal granularity and abundant semantic information. The increasing availability of big spatial flow has brought us with unpre作者: 中古 時間: 2025-3-22 21:59
Outdoor Education and Thermal Comfortin a database system. Such a trajectory contains a sequence of time-stamped locations and a set of descriptive attributes. Multi-attribute trajectories mainly deal with attributes describing characteristics of objects, i.e., attributes that are not relevant to locations. This differs from semantic a作者: 摘要 時間: 2025-3-23 03:23 作者: Agronomy 時間: 2025-3-23 07:31
K. V. Rao,Sigurds Arajs,D. Abukayon. Traditional methods of map vector data generation based on surveyor’s field work and map digitalization are costly and have a long update period. In the Big Data age, large-scale GPS-enabled taxi trajectories and high-volume ride-sharing datasets are increasingly available. These datasets provid作者: AVOW 時間: 2025-3-23 12:51
R. D. Redin,T. Ashworth,C. Y. Hsiungrstand movement processes are confronted with growing movement datasets. Yet, there are no established analysis tools that support analysts in understanding these datasets and in asking the right analysis questions. Exploratory data analysis (EDA) is an approach that helps analysts to identify the m作者: outer-ear 時間: 2025-3-23 17:48 作者: 戰(zhàn)勝 時間: 2025-3-23 19:01 作者: 600 時間: 2025-3-24 01:59 作者: 愚笨 時間: 2025-3-24 03:51 作者: 背心 時間: 2025-3-24 07:44 作者: 蒸發(fā) 時間: 2025-3-24 11:22 作者: Cuisine 時間: 2025-3-24 17:39 作者: galley 時間: 2025-3-24 20:40
Spatial Data Reduction Through Element-of-Interest (EOI) Extractionntaining geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data domain can be summarized as the ‘Big Vs’ – variety, volume, ve作者: ILEUM 時間: 2025-3-25 02:21 作者: Cerumen 時間: 2025-3-25 06:59
Big Spatial Flow Data Analytics recent years, the widespread adoption of location-aware technologies such as the GPS-enabled smartphones amass flow data at individual level, along with much finer spatiotemporal granularity and abundant semantic information. The increasing availability of big spatial flow has brought us with unpre作者: 眨眼 時間: 2025-3-25 10:29 作者: 狗舍 時間: 2025-3-25 13:46
Mining Colocation from Big Geo-Spatial Event Data on GPUhose instances are frequently located together. The problem is important in many applications such as analyzing relationships of crimes or disease with various environmental factors, but is computationally challenging due to a large number of instances, the potentially exponential number of candidat作者: Gobble 時間: 2025-3-25 16:53 作者: 施加 時間: 2025-3-25 22:50
Exploratory Analysis of Massive Movement Datarstand movement processes are confronted with growing movement datasets. Yet, there are no established analysis tools that support analysts in understanding these datasets and in asking the right analysis questions. Exploratory data analysis (EDA) is an approach that helps analysts to identify the m作者: Ethics 時間: 2025-3-26 00:13
Spatio-Temporal Data Quality: Experience from Provision of DOT Traveler Informationsportation Institute, and now Utah State University. Ian Turnbull, recently retired from Caltrans, set the standard of “accurate, timely and reliable” for all projects he was involved with, particularly those in remote, rural areas of California, and related collaborations. Ian’s team, now headed by作者: 噱頭 時間: 2025-3-26 07:25 作者: LAVE 時間: 2025-3-26 08:41
Spatial Statistics, or How to Extract Knowledge from Datartant to prove theoretical/physical models. For this reason, we initially describe classical modeling approaches in geostatistics and spatial econometrics. Moreover, we show how large spatial and spatiotemporal data can be modeled by these approaches. Therefore, we sketch selected suitable methods a作者: 斥責(zé) 時間: 2025-3-26 16:33
978-3-030-55464-4Springer Nature Switzerland AG 2021作者: 字的誤用 時間: 2025-3-26 19:15
Martin Werner,Yao-Yi ChiangCovers current research in accessible language including references to the field, and therefore provides an ideal collection to explore the field of big geospatial data.Spans across traditionally isol作者: occult 時間: 2025-3-27 00:55
http://image.papertrans.cn/h/image/420876.jpg作者: GIST 時間: 2025-3-27 03:21
https://doi.org/10.1007/978-3-030-55462-0Algorithms for Big Data; Geographic Information Science; Uncertain Databases; Spatial Databases; Histori作者: 名字 時間: 2025-3-27 07:35 作者: 竊喜 時間: 2025-3-27 10:15
Semantic Trajectories Data ModelsSemantic trajectories is a major paradigm for the representation of movement data, complementary to spatial trajectories. In this article, we introduce key concepts, focusing in particular on the structural properties of semantic trajectories. Hence, we discuss a possible taxonomy of semantic trajectory models, based on their purpose.作者: chance 時間: 2025-3-27 14:42
Book 2021l data: a societal, governmental, and governance perspective. It discusses questions ofhow the acquisition, distribution and exploitation of big geospatial data must be organized both on the scale of companies and countries. A second perspective is a theory-oriented set of contributions on arbitrary作者: 飛行員 時間: 2025-3-27 20:44
the acquisition, distribution and exploitation of big geospatial data must be organized both on the scale of companies and countries. A second perspective is a theory-oriented set of contributions on arbitrary978-3-030-55464-4978-3-030-55462-0作者: motor-unit 時間: 2025-3-27 23:34 作者: Aids209 時間: 2025-3-28 02:56
https://doi.org/10.1007/978-3-0348-6719-1 al. .), and variation in sea levels (Woodworth et al. .), (2) Urban planning: assisting government in city/regional planning, road network design, and transportation/traffic engineering, (3) Commerce and advertisement (Dhar and Varshney .): e.g., point-of-interest (POI) recommendation services. The作者: 釘牢 時間: 2025-3-28 08:31
Atoms, Small, and Large Molecules,sents current state-of-the-art methods to create EOI from some types of georeferenced big data. We classify the data types into two realms: active and passive. Active data are those collected specifically for the purpose to which they are applied. Passive data are those collected for purposes other 作者: ABIDE 時間: 2025-3-28 13:15
Alhussein Albarbar,Canras Batunluhapter, we investigate solutions relying on the Semantic Web technologies for the description of the evolution of geographic divisions over time. We investigate how these technologies may enhance the understanding of the territorial dynamics over time, providing statisticians, researchers, citizens 作者: 的闡明 時間: 2025-3-28 17:38 作者: Ophthalmoscope 時間: 2025-3-28 21:18
Semiconductors and Thermoelectric Materials which is economically expensive, and their reducer nodes have a bottleneck of aggregating all instances of the same colocation patterns. Another work proposes a parallel colocation mining algorithm on GPU based on the iCPI tree and the joinless approach, but assumes that the number of neighbors for作者: CHOP 時間: 2025-3-28 23:44
K. V. Rao,Sigurds Arajs,D. Abukayd with the road layer downloaded from OpenStreetMap. We measure the quality and demonstrate the effectiveness of our road extraction method regarding accuracy, spatial coverage and connectivity. The proposed framework shows a good potential to update fundamental road transportation information for s作者: expansive 時間: 2025-3-29 05:02 作者: 取消 時間: 2025-3-29 07:38 作者: Exposition 時間: 2025-3-29 13:26
IBM PAIRS: Scalable Big Geospatial-Temporal Data and Analytics As-a-Serviceds of PetaBytes of data, (ii) harmonization of data in order to mask the complexity of data (schema, map projection etc.) from end users, (iii) advanced search capabilities of data at a “pixel level” (in contrast to “file level”), and (iv) “in-data” analytics and computation to avoid downloading the作者: anthesis 時間: 2025-3-29 19:26
Big Geospatial Data Processing Made Easy: A Working Guide to GeoSpark al. .), and variation in sea levels (Woodworth et al. .), (2) Urban planning: assisting government in city/regional planning, road network design, and transportation/traffic engineering, (3) Commerce and advertisement (Dhar and Varshney .): e.g., point-of-interest (POI) recommendation services. The作者: 跑過 時間: 2025-3-29 20:16 作者: 辯論的終結(jié) 時間: 2025-3-30 03:45 作者: OWL 時間: 2025-3-30 06:33
Big Spatial Flow Data Analytics mining, and spatial statistics, to give readers a comprehensive picture of the available approaches that serve different study purposes. One representative approach from each family is selected to elaborate, so the readers can gain a deeper understanding to readily use the methods and potentially d作者: Credence 時間: 2025-3-30 10:38 作者: 集合 時間: 2025-3-30 14:53 作者: 側(cè)面左右 時間: 2025-3-30 16:53
