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內(nèi)容簡介

　　大數(shù)據(jù)時代已經(jīng)到來，隨著數(shù)據(jù)的逐步開放，交通領(lǐng)域的研究課題或多或少都要接觸、使用時空大數(shù)據(jù)。交通領(lǐng)域的從業(yè)者迫切需要強有力的工具和技術(shù)應(yīng)對日益紛雜的交通數(shù)據(jù)。交通是一個交叉學(xué)科，交通數(shù)據(jù)分析人才的知識體系需要與數(shù)據(jù)處理、網(wǎng)絡(luò)爬蟲、數(shù)據(jù)可視化、地理信息、復(fù)雜網(wǎng)絡(luò)、數(shù)據(jù)挖掘、機器學(xué)習(xí)等多學(xué)科知識深度融合，這也為交通領(lǐng)域的人才培養(yǎng)帶來巨大挑戰(zhàn)。在此背景下，本書針對不同的學(xué)習(xí)階段與業(yè)務(wù)需求設(shè)計了三篇共15章內(nèi)容?；A(chǔ)篇（第1～5章）梳理Python數(shù)據(jù)分析、網(wǎng)絡(luò)爬蟲、數(shù)據(jù)可視化、地理信息等基礎(chǔ)知識；應(yīng)用篇（第6～10章）介紹出租車GPS數(shù)據(jù)、地鐵IC刷卡數(shù)據(jù)、共享單車訂單數(shù)據(jù)、公交GPS數(shù)據(jù)等各類時空大數(shù)據(jù)的實際案例應(yīng)用；方法篇（第11～15章）融匯數(shù)據(jù)挖掘、空間統(tǒng)計、復(fù)雜網(wǎng)絡(luò)學(xué)科等交叉學(xué)科方法，與交通領(lǐng)域的大量實際案例分析結(jié)合，全面梳理總結(jié)交通時空大數(shù)據(jù)所需跨學(xué)科技能。本書由淺入深，學(xué)科交叉，強調(diào)實踐。對讀者不同的學(xué)習(xí)階段與業(yè)務(wù)需求設(shè)計相應(yīng)內(nèi)容，全面梳理總結(jié)交通大數(shù)據(jù)科研所需技能，并與交通領(lǐng)域的大量實際案例分析結(jié)合。本書可作為教材也可作為參考工具書，基礎(chǔ)篇定位交通數(shù)據(jù)領(lǐng)域新手入門，應(yīng)用篇定位有數(shù)據(jù)分析需求的高校學(xué)生或社會人士，方法篇定位高校學(xué)術(shù)科研人員。

作者簡介

　　余慶（交通數(shù)據(jù)小旭學(xué)長）博士，南方科技大學(xué)斯發(fā)基斯可信自主系統(tǒng)研究院助理研究員，交通時空大數(shù)據(jù)開源Python庫TransBigData作者。B站交通時空大數(shù)據(jù)相關(guān)視頻課程總播放量超過80萬。2022年博士畢業(yè)于同濟大學(xué)交通運輸工程專業(yè)，博士期間赴日本東京大學(xué)公派聯(lián)合培養(yǎng)，主要研究方向為交通大數(shù)據(jù)分析、數(shù)據(jù)可視化、城市計算，發(fā)表SCI論文十余篇。自2020年起在B站上制作交通時空大數(shù)據(jù)相關(guān)課程，涵蓋時空數(shù)據(jù)處理、數(shù)據(jù)可視化等。李瑋峰同濟大學(xué)交通運輸工程學(xué)院助理研究員，博士。主要研究方向為交通規(guī)劃、智能交通系統(tǒng)規(guī)劃和交通大數(shù)據(jù)分析。參加國家重點研發(fā)計劃項目1項、國家自然科學(xué)基金重點項目2項、面上項目2項，國家科技支撐計劃項目2項，同時參加地方政府和科研院所的研究與咨詢項目多項。發(fā)表期刊及會議論文50余篇，其中SCI收錄16篇、EI收錄20余篇；完成專著3本；獲得發(fā)明專利4項，軟件著作權(quán)3項。

圖書目錄

目　錄
基礎(chǔ) 篇
第1章　緒論 ·····························2
1.1 多源交通時空大數(shù)據(jù)簡介 ················2
1.1.1 傳統(tǒng)集計統(tǒng)計數(shù)據(jù) ·······························3
1.1.2 個體連續(xù)追蹤數(shù)據(jù) ·······························4
1.1.3 地理空間信息數(shù)據(jù) ·······························5
1.2 為什么要用Python處理交通大數(shù)據(jù) ·····6
1.2.1 常用數(shù)據(jù)處理技術(shù) ·······························6
1.2.2 Python在交通大數(shù)據(jù)領(lǐng)域中的優(yōu)勢 ····8
1.2.3 Python與SQL的比較 ····························9
1.3 大規(guī)模數(shù)據(jù)處理的解決方案··············9
1.3.1 決定大數(shù)據(jù)處理性能的三個硬件
要素 ·······················································9
1.3.2 分布式數(shù)據(jù)處理架構(gòu) ·························11
1.4 本章習(xí)題 ···································14
第2章　Python數(shù)據(jù)處理基礎(chǔ) ······15
2.1 Python的環(huán)境配置 ························15
2.1.1 Python的集成開發(fā)環(huán)境 ······················15
2.1.2 Anaconda的安裝 ·································16
2.1.3 Jupyter Notebook的使用 ·····················16
2.1.4 Python第三方庫的安裝 ······················18
2.2 Python基本語法 ···························19
2.2.1 對象與變量 ·········································19
2.2.2 運算符 ·················································20
2.2.3 內(nèi)置數(shù)據(jù)類型 ·····································20
2.2.4 語句 ·····················································24
2.2.5 函數(shù) ·····················································26
2.2.6 包的使用 ·············································27
2.2.7 數(shù)據(jù)分析常用第三方庫簡介 ·············28
2.3 pandas數(shù)據(jù)處理基礎(chǔ) ·····················29
2.3.1 數(shù)據(jù)文件的編碼格式與存儲形式 ·····30
2.3.2 數(shù)據(jù)表的行列處理 ·····························33
2.3.3 數(shù)據(jù)的表格運算 ·································41
2.4 時空大數(shù)據(jù)的處理思維 ·················46
2.4.1 復(fù)雜數(shù)據(jù)處理任務(wù)的解決思路 ·········46
2.4.2 數(shù)據(jù)處理任務(wù)分解實例：地鐵換乘量
識別 ······················································49
2.5 數(shù)據(jù)處理中表格運算的常用技巧 ······51
2.5.1 分組編號 ·············································51
2.5.2 去除重復(fù)的記錄 ·································53
2.5.3 個體ID重新編號 ·································54
2.5.4 生成數(shù)據(jù)之間的對應(yīng)表 ·····················55
2.5.5 時空插值 ·············································58
2.6 本章習(xí)題 ···································60
2.6.1 思考題 ·················································60
2.6.2 Python基礎(chǔ)代碼練習(xí) ··························60
2.6.3 pandas基礎(chǔ)代碼練習(xí) ··························62
第3章　數(shù)據(jù)可視化基礎(chǔ) ············64
3.1 可視化的基本原則 ·······················64