找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Data Science for Transport; A Self-Study Guide w Charles Fox Textbook 2018 Springer International Publishing AG 2018 Quantitative Geography

[復(fù)制鏈接]
查看: 19394|回復(fù): 47
樓主
發(fā)表于 2025-3-21 17:51:26 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Science for Transport
副標(biāo)題A Self-Study Guide w
編輯Charles Fox
視頻videohttp://file.papertrans.cn/264/263123/263123.mp4
概述Introduces data science for students of transport studies, geography and the geosciences, as well as transport professionals.The only book to integrate the whole stack of transport data analysis.Addre
叢書名稱Springer Textbooks in Earth Sciences, Geography and Environment
圖書封面Titlebook: Data Science for Transport; A Self-Study Guide w Charles Fox Textbook 2018 Springer International Publishing AG 2018 Quantitative Geography
描述The quantity, diversity and availability of transport data is increasing rapidly, requiring?new skills in the management and interrogation of data and databases. Recent years?have seen a new wave of ‘big data‘, ‘Data Science‘, and ‘smart cities‘ changing the?world, with the Harvard Business Review describing Data Science as the "sexiest job of?the 21st century". Transportation professionals and researchers need to be able to use?data and databases in order to establish quantitative, empirical facts, and to validate?and challenge their mathematical models, whose axioms have traditionally often been?assumed rather than rigorously tested against data. This book takes a highly practical?approach to learning about Data Science tools and their application to investigating?transport issues. The focus is principally on practical, professional work with real data?and tools, including business and ethical issues..".Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am
出版日期Textbook 2018
關(guān)鍵詞Quantitative Geography; Transport Studies; Data Science for Geography and Geoscience; Machine Learning
版次1
doihttps://doi.org/10.1007/978-3-319-72953-4
isbn_softcover978-3-030-10291-3
isbn_ebook978-3-319-72953-4Series ISSN 2510-1307 Series E-ISSN 2510-1315
issn_series 2510-1307
copyrightSpringer International Publishing AG 2018
The information of publication is updating

書目名稱Data Science for Transport影響因子(影響力)




書目名稱Data Science for Transport影響因子(影響力)學(xué)科排名




書目名稱Data Science for Transport網(wǎng)絡(luò)公開度




書目名稱Data Science for Transport網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Science for Transport被引頻次




書目名稱Data Science for Transport被引頻次學(xué)科排名




書目名稱Data Science for Transport年度引用




書目名稱Data Science for Transport年度引用學(xué)科排名




書目名稱Data Science for Transport讀者反饋




書目名稱Data Science for Transport讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 23:30:13 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:27:37 | 只看該作者
地板
發(fā)表于 2025-3-22 04:52:28 | 只看該作者
Database Design,During the Python exercise, we used text processing operations to step through a CSV file and process each line at a time. For some applications, this method is scaled up to store and process larger data sets. For example we might have a separate CSV file for each year’s road accidents for many years, and perhaps also for many countries.
5#
發(fā)表于 2025-3-22 10:46:08 | 只看該作者
Spatial Data,Transport data is generally about motion through time and space. The previous chapter considered the complexities of representing time – here we will think about space.
6#
發(fā)表于 2025-3-22 14:26:58 | 只看該作者
7#
發(fā)表于 2025-3-22 17:03:22 | 只看該作者
8#
發(fā)表于 2025-3-23 00:52:39 | 只看該作者
9#
發(fā)表于 2025-3-23 04:29:47 | 只看該作者
10#
發(fā)表于 2025-3-23 07:29:00 | 只看該作者
Data Visualisation,e for the payoff: visualizing the results in full colour! This chapter will give a short overview of relevant human visual perception, present a “gallery” of classic transport-related data visualizations, then show how to produce some of your own.
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-15 06:38
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
阜阳市| 甘泉县| 北辰区| 高阳县| 峨山| 阳信县| 鹤庆县| 柳河县| 灵武市| 新余市| 夏河县| 嵊州市| 台前县| 牟定县| 环江| 木兰县| 阜阳市| 嘉定区| 南汇区| 新平| 旬阳县| 金坛市| 湘西| 乌海市| 成安县| 尤溪县| 长宁区| 元谋县| 新泰市| 东海县| 贺兰县| 宜都市| 繁昌县| 太仆寺旗| 沧源| 舒城县| 乌苏市| 安仁县| 朝阳市| 伊宁市| 扶余县|