時(shí)空大數(shù)據(jù)(spatiotemporal big data)、大數(shù)據(jù)平臺(tái)(big data platform)、大數(shù)據(jù)分析(big data analysis)和大數(shù)據(jù)應(yīng)用(big data application)是近年來(lái)引起各行各業(yè)普遍關(guān)注的重要領(lǐng)域，相關(guān)的研究成果涉及時(shí)空大數(shù)據(jù)內(nèi)涵解析與發(fā)展機(jī)遇、大數(shù)據(jù)平臺(tái)構(gòu)建與數(shù)據(jù)管理、大數(shù)據(jù)分析技術(shù)與數(shù)據(jù)挖掘、大數(shù)據(jù)應(yīng)用探索與決策支持等多個(gè)方面。

在城鄉(xiāng)景觀規(guī)劃設(shè)計(jì)研究領(lǐng)域，時(shí)空大數(shù)據(jù)的應(yīng)用也逐步受到重視，相關(guān)探索性研究既涉及大數(shù)據(jù)在城鄉(xiāng)規(guī)劃設(shè)計(jì)領(lǐng)域應(yīng)用的技術(shù)與方法，也涉及大數(shù)據(jù)時(shí)代風(fēng)景園林的發(fā)展和教育；研究范圍既有大中尺度的風(fēng)景名勝區(qū)及新型城鎮(zhèn)化背景下的城市園林規(guī)劃設(shè)計(jì)，也有關(guān)注使用者活動(dòng)方式的中小尺度的城市綠地系統(tǒng)規(guī)劃設(shè)計(jì)；特別是在將多種類型的時(shí)空大數(shù)據(jù)應(yīng)用于景觀規(guī)劃設(shè)計(jì)的具體案例中，手機(jī)信令數(shù)據(jù)的應(yīng)用、衛(wèi)星定位導(dǎo)航(GNSS)數(shù)據(jù)的應(yīng)用、社交網(wǎng)絡(luò)(SNS)大數(shù)據(jù)的應(yīng)用、具有地理位置的景觀照片(geo-tagged photo)分析，以及基于社會(huì)感知大數(shù)據(jù)的人群行為模式(behavioral mode)分析等，均表現(xiàn)出全方位的時(shí)空大數(shù)據(jù)輔助景觀規(guī)劃設(shè)計(jì)的態(tài)勢(shì)。

時(shí)空大數(shù)據(jù)的出現(xiàn)為景觀規(guī)劃設(shè)計(jì)帶來(lái)了新的機(jī)遇和挑戰(zhàn)。機(jī)遇體現(xiàn)在時(shí)空大數(shù)據(jù)為我們開(kāi)展景觀規(guī)劃設(shè)計(jì)提供了全面、系統(tǒng)、定量地分析與認(rèn)識(shí)場(chǎng)地的機(jī)會(huì)，特別是認(rèn)識(shí)人與場(chǎng)地的關(guān)系，因?yàn)闀r(shí)空大數(shù)據(jù)類型多樣、內(nèi)容豐富、特征鮮明，與城鄉(xiāng)景觀規(guī)劃設(shè)計(jì)相關(guān)的時(shí)空大數(shù)據(jù)包括2個(gè)大類(靜態(tài)數(shù)據(jù)與動(dòng)態(tài)數(shù)據(jù))、12個(gè)中類(基礎(chǔ)空間數(shù)據(jù)、場(chǎng)地資源數(shù)據(jù)、場(chǎng)地設(shè)施數(shù)據(jù)、社會(huì)經(jīng)濟(jì)數(shù)據(jù)、環(huán)境效益數(shù)據(jù)、人流統(tǒng)計(jì)數(shù)據(jù)、動(dòng)態(tài)監(jiān)測(cè)數(shù)據(jù)、定位通信數(shù)據(jù)、網(wǎng)絡(luò)媒體數(shù)據(jù)、社交網(wǎng)絡(luò)數(shù)據(jù)、刷卡消費(fèi)數(shù)據(jù)、活動(dòng)行為數(shù)據(jù))、30多個(gè)小類；不僅如此，時(shí)空大數(shù)據(jù)具有6個(gè)方面的顯著特性，即客觀性、多源性、動(dòng)態(tài)性、精細(xì)性、現(xiàn)勢(shì)性和人本性，這使得數(shù)字景觀規(guī)劃設(shè)計(jì)迎來(lái)新的發(fā)展契機(jī)。于是，業(yè)內(nèi)不少學(xué)者開(kāi)展了基于時(shí)空大數(shù)據(jù)的景觀規(guī)劃設(shè)計(jì)探討，涵蓋了基于移動(dòng)通信大數(shù)據(jù)的城市公園游客構(gòu)成及活動(dòng)分析與規(guī)劃設(shè)計(jì)研究、基于定位導(dǎo)航大數(shù)據(jù)的歷史文化景區(qū)游客空間行為模式分析與規(guī)劃設(shè)計(jì)研究、基于社交網(wǎng)絡(luò)大數(shù)據(jù)的城市綠地系統(tǒng)分析與公園綠地規(guī)劃設(shè)計(jì)研究、基于環(huán)境感知大數(shù)據(jù)的城市生態(tài)系統(tǒng)變遷分析與規(guī)劃管理研究、基于數(shù)值模擬大數(shù)據(jù)的居住小區(qū)建筑布局與環(huán)境規(guī)劃設(shè)計(jì)研究，以及基于地理標(biāo)記景觀照片大數(shù)據(jù)的街道尺度景觀規(guī)劃設(shè)計(jì)等。

在看到探索研究成果的同時(shí)，也要關(guān)注到景觀規(guī)劃設(shè)計(jì)領(lǐng)域時(shí)空大數(shù)據(jù)應(yīng)用面臨的挑戰(zhàn)，主要體現(xiàn)在2個(gè)方面，其一是大數(shù)據(jù)的獲取途徑還比較有限，很多的景觀時(shí)空大數(shù)據(jù)存儲(chǔ)在事業(yè)或者企業(yè)服務(wù)器中，對(duì)于廣大景觀規(guī)劃設(shè)計(jì)從業(yè)者來(lái)說(shuō)，并沒(méi)有真正的時(shí)空大數(shù)據(jù)可以隨時(shí)應(yīng)用；其二是大數(shù)據(jù)的處理能力還比較有限，多數(shù)景觀規(guī)劃從業(yè)人員對(duì)于大數(shù)據(jù)分析和應(yīng)用的技能還沒(méi)有達(dá)到預(yù)期水準(zhǔn)，所以也無(wú)法在自己承擔(dān)的景觀規(guī)劃設(shè)計(jì)研究中嘗試應(yīng)用大數(shù)據(jù)。然而，面向未來(lái)，上述挑戰(zhàn)將逐步予以解決，因?yàn)閲?guó)家已經(jīng)出臺(tái)了《促進(jìn)大數(shù)據(jù)發(fā)展行動(dòng)綱要》，城鄉(xiāng)景觀規(guī)劃設(shè)計(jì)領(lǐng)域已經(jīng)呈現(xiàn)出以下5個(gè)方面的時(shí)空大數(shù)據(jù)應(yīng)用發(fā)展趨勢(shì)：其一是大數(shù)據(jù)應(yīng)用的生態(tài)環(huán)境逐步構(gòu)建，其二是大數(shù)據(jù)應(yīng)用的技術(shù)體系逐步形成，其三是大數(shù)據(jù)應(yīng)用的人才力量逐步壯大，其四是大數(shù)據(jù)應(yīng)用的多源集成創(chuàng)新發(fā)展，其五是大數(shù)據(jù)應(yīng)用的人本主義特色體現(xiàn)?；跁r(shí)空大數(shù)據(jù)的景觀規(guī)劃設(shè)計(jì)必將體現(xiàn)人本主義特色，通過(guò)大數(shù)據(jù)輔助分析人的自然、社會(huì)、文化、情感等多維特性，然后通過(guò)規(guī)劃設(shè)計(jì)的手法，在空間、時(shí)間、設(shè)施、環(huán)境等多個(gè)維度滿足人的需求特性。

最后，感謝清華大學(xué)黨安榮教授對(duì)本期主題文章的貢獻(xiàn)。

Spatiotemporal big data, big data platform, big data analysis and big data application are important areas of interest that have drawn the attention of a wide range of industries in recent years. Relevant research results include many aspects such as the big data connotation analysis and development opportunity, the construction and data management of big data platform, big data analysis technology and data mining, big data application exploration and decision support.

In the field of urban and rural landscape planning and design, the application of spatiotemporal big data has also been gradually taken seriously. Relevant exploratory research involves both technologies and methods big data applied in urban and rural planning and design as well as the development and education of landscape architecture in the era of big data. The research range includes both large and medium-sized scenic areas and urban landscape planning and design under new urbanization, as well as small and medium sized urban green space system planning and design that focuses on user activities. Particularly in the cases of the application of multiple types of spatiotemporal big data to the landscape planning and design, the application of handset signaling data,GNSS data, SNS big data, geo-tagged photo analysis, and behavioral mode analysis based on social perception big data, show the situation of the full range spatiotemporal big data aiding landscape planning and design.

The advent of spatiotemporal big data brings new opportunities and challenges to landscape planning and design. The opportunities are reflected in the spatiotemporal big data providing a comprehensive, systematic and quantitative analysis and understanding of venues for our landscape planning and design, especially in understanding the relationship between people and venues, because the spatiotemporal big data are of diverse types,rich contents, and distinctive features, The spatiotemporal big data related to urban and rural landscape planning and design include two large categories (static data and dynamic data), 12 medium categories (basic spatial data, site resource data, site facilities data, socio-economic data, environmental benefits data, people flow statistics, dynamic monitoring data, positioning communication data, network media data, social network data, credit card spending data, and activity data), and over 30 sub-categories; not only that, the spatiotemporal big data has six aspects of significant features, such as being objective, multi-source, dynamic, potential, sophisticated and human-oriented, bringing digital landscape planning and design a new development opportunity. Thus, we see that many scholars in the industry have carried out the discussion on the landscape planning and design based on the spatiotemporal big data, covering the areas of the composition and activity analysis and planning and design of tourists in urban parks based on the big data of mobile communication, the spatial behavior pattern analysis and planning and design research of tourists in historical and cultural scenic areas based on the big data of positioning and navigation, the urban green space system analysis and park green space planning and design research based on the social network big data, the urban ecosystem change analysis and planning management research based on the environmental perception big data, the residential building layout and environmental planning and design research based on the numerical simulation big data, as well as the street scale landscape planning and design based on the geo-tagging landscape photo big data.

While seeing the achievements of exploration and research, we should also pay attention to the challenges faced in the application of spatiotemporal big data in the field of landscape planning and design, mainly in two aspects. One is that the access to big data is still relatively limited, and a lot of landscape spatiotemporal big data are stored in institutional or enterprise servers, and for the majority of landscape design and planning practitioners, no real spatiotemporal big data can be readily applied; the second is the processing power of big data is still relatively limited, and most landscape planning practitioners' skills of big data analysis and application have not yet reached the expected level, analysis, so they cannot try to apply big data in their own landscape planning and design studies. However, facing the future, the above-mentioned challenges will be gradually solved, since the state has promulgated the Action Plan for Promoting Big Data Development, and the urban and rural landscape planning and design field has shown the following five trends in the application of spatiotemporal big data: 1) the eco-environment of big data application gradually building up, 2) the technology system of big data application gradually taking shape, 3) the talent strength of big data application gradually expanding, 4) the multi-source integration of big data application innovatively developing, and 5) the human-oriented characteristics of big data application showing up. The landscape planning and design based on spatiotemporal big data is bound to embody the human-oriented characteristics, help to analyze people’s natural, social, cultural and emotional multidimensional features through big data, and then use planning and design techniques in space, time, facilities, environment, and multiple dimensions to meet the people's needs.

Last but not the least, we are grateful to Professor Dang Anrong of Tsinghua University for his contributions to the thematic papers in this issue.