EN中文

Journal of Geo-information Science >

2018 , Vol. 20 >Issue 6: 854 - 861

DOI: https://doi.org/10.12082/dqxxkx.2018.180020

Urban Spatial Structure of Port City in South China Sea Based on Spatial Coupling between Nighttime Light Data and POI

  • YU Bingchen , 1, 2, * ,
  • LIU Yuxuan 1, 2 ,
  • CHEN Gang 1, 2
Expand
  • 1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
  • 2. Collaborative Innovation Center of South China Sea Studies, Nanjing 210023, China
*Corresponding author: YU Bingchen, E-mail:

Received date: 2018-01-02

  Request revised date: 2018-02-12

  Online published: 2018-06-20

Supported by

Major Project of the National Social Science Fund of China, No.14ZDA078

Major Project on Philosophy and Social Science of Universities in Jiangsu, No.2013ZDAXM008

Copyright

《地球信息科学学报》编辑部 所有
Fold

Abstract

Port city in South China Sea, the important transportation hub in South China Sea and the 21st Century Maritime Silk Road, is significant for research on resource and environment monitoring of South China Sea. However, there is a lack of research about urban spatial structure of port city in South China Sea, especially the role of the port in urban spatial structure. Both of nighttime light data and POI (Point of Interest) data are widely used in research on urban spatial structure as data source, but few research focus on the spatial coupling between nighttime light data and POI data, and the integrated application of two data sources. As an example on the spatial coupling between nighttime light data and POI data, we take Sanya City, a typical representative port city in South China Sea, as study area, and use NPP-VIIRS nighttime light data and POI data of study area in 2016 as data source. In addition, we use overlay analysis to transform both data, the nighttime light data and the processed POI data by kernel density method, into regular grids. Then we use the method for mapping double factors to discuss the spatial coupling relationship between both data, and analyze the relationship between areas with different spatial couplings and urban spatial structures, especially the ports. The results show that: (1) The nighttime light data and POI data have strong spatial coupling relationship, which indicates a significant consistency. The global trend of spatial distribution between both data in Sanya City is pretty similar, and 85.6% of total area have same spatial coupling. (2) The regions where spatial coupling between nighttime light data and POI data differs have some significant spatial characteristics of urban structures, such as the large-scale homogeneous regions, rural-urban fringe, suburbs, and township. POI data has few distributions in economic development zone, new urban districts and so on, but much distribution in suburbs, townships and so on. By contrast, nighttime light data can characterize urban construction significantly, but can't characterize the township. (3) Sanya City, as an important port city in South China Sea, shows a strong relationship between its urban centers and ports. All of three urban centers revolve around three main ports and spread from it in a ring or line form. This study provides a new perspective of the research ont urban spatial structure of port cities in South China Sea.

Key words： nighttime light data; POI; spatial coupling; Sanya City; port city in South China Sea; urban spatial structure

Cite this article

YU Bingchen , LIU Yuxuan , CHEN Gang . Urban Spatial Structure of Port City in South China Sea Based on Spatial Coupling between Nighttime Light Data and POI[J]. Journal of Geo-information Science, 2018 , 20(6) : 854 -861 . DOI: 10.12082/dqxxkx.2018.180020

1 引言

南海港口城市的研究对了解南海周边资源环境具有重要意义[1],同时也可以为21世纪海上丝绸之路提供决策支持[2]。三亚市是南海周边重要港口城市,也是著名的旅游城市,因而选取其作为典型研究区。
南海港口城市的研究方面,学者已经做了一定的研究工作。研究内容包括南海港口城市的海岸带研究[3],南海港口城市土地利用变化研究[4],南海港口城市间的经济合作研究[5]等。目前,对于南海港口城市空间结构特别是港口在南海港口城市结构中的地位等研究则存在不足。
夜光遥感和POI数据是城市空间结构研究的常用数据源。夜光遥感数据具有数据量丰富、时空连续、覆盖范围广、独立客观等特点[6],在城市群的扩张研究[7,8]、城市经济产业发展研究[9]、城市的演化发展研究[10,11]、城市体系等级格局[12]等领域得到了广泛应用。POI数据以其数据属性丰富、更新速度较快、获取成本较低等特点[13],在城市建成区提取[13]、城市功能区识别[14,15]、城市产业空间格局[16,17]、城市混合用地研究[18]、“鬼城”识别研究[19]等领域得到了一定应用。同时,也有学者开展了对夜光遥感和POI数据的集成应用研究[20],2种数据均在城市建成区提取[7,13]、城市经济产业发展[9,16]等研究中同时得到了应用,但目前对二者空间耦合关系的讨论仍较为缺乏。
针对南海港口城市空间结构以及夜光遥感与POI数据耦合关系等研究缺乏的现状,本文以典型南海港口城市三亚市为研究区,基于NPP-VIIRS夜光遥感数据和POI数据,利用数据网格化和双变量制图等方法,分析两种数据的空间耦合关系,探究不同类型的空间耦合关系和城市空间结构之间的关联,并在此基础上探讨港口在城市空间结构中的地位。

2 研究区概况与数据源

2.1 研究区概况

三亚市位于海南岛最南端,南临南海,是中国除三沙市外最南端的城市。三亚市面积1919 km2,人口57万（2016年）,现下辖海棠、吉阳、崖州和天涯4个区,是海南岛南部中心城市,研究区示意图如图1所示,其中红色区域为一些重要的城市功能区。
Fig. 1 Map of study area

图1 研究区示意图

2014年,三亚市进行行政区划调整,撤销了除育才镇之外的全部乡镇和管理区,并设立4个市辖区。但原乡镇是城市空间结构研究的重要组成,故在图1中予以保留。由于三亚市周边的近海岛屿面积狭小且数据缺乏,因而未将这些岛屿纳入到研究范围中。
三亚市是重要的南海港口城市和21世纪海上丝绸之路节点城市,在21世纪海上丝绸之路建设与南海战略研究中具有重要地位。三亚市海岸线209.1 km,拥有三亚湾、海棠湾、崖州湾、亚龙湾和红塘湾等19个港湾,主要港口有三亚港、南山港和铁炉港等,其中三亚港为国家对外开放一类口岸。
三亚市中心城区位于三亚湾周边,是三亚市政府驻地,天涯区和吉阳区政府驻地也位于中心城区。崖州区政府驻地为历史上崖州府所在地,而海棠区政府驻地为开发的新城区。此外,三亚市拥有国际机场——凤凰国际机场,亚龙湾和海棠湾等旅游开发区,以及三亚市创意产业园等经济开发区。

2.2 数据源

数据源包括基础地理数据、夜光遥感数据和POI数据。基础地理数据包括三亚市行政区划边界和三亚市各级政府驻地,参考海南省测绘地理信息局三亚市标准地图（http://www.hbsm.gov.cn/wsfw_7045/bzdt/201702/t20170216_384728.html）和三亚市天地图（http://www.sanyamap.cn）等地图资料,使用GIS软件数字化采集获得。
夜光遥感数据源为NPP-VIIRS传感器获取的夜光遥感数据,其数据量极其丰富并且可免费获取,数据空间分辨率约为740 m[6]。NPP-VIIRS夜光遥感数据作为新型夜光遥感数据源,在城市研究中得到了广泛应用[21]。该数据产品包含日平均数据和月平均数据等,本文使用的为2016年1-12月各月中国区域数据的月平均数据,共12景数据。对获取的各数据取平均值,并利用三亚市行政区划边界进行裁切,之后将数据重采样至500 m,最终得到500 m空间分辨率的三亚市2016年年平均夜光遥感数据。
POI数据源自三亚市天地图提供的API接口,为海南省测绘地理信息局官方采集的数据,具有较高的权威性,数据获取时间为2016年。通过该接口共获取三亚市境内POI数据26 047条,数据分为 16个大类（表1）。本文选取与城镇等密切相关的类别,对其他类别进行排除,以更好地研究与夜光遥感数据的耦合关系。最后,排除了旅游景点、政府机构、地理地名、农场牧场等类别,共保留了20 174条数据。
Tab. 1 Classification and counts of POI

表1 POI类别与数量表

编号 1 2 3 4 5 6 7 8
类别 餐饮饭店 泊车汽修 休闲娱乐 小区大厦 商铺超市 生活服务 医药卫生 宾馆住宿
数量/个 3458 1155 595 1183 7872 2023 768 1191
编号 9 10 11 12 13 14 15 16
类别 旅游景点 政府部门 教育机构 公交站点 银行金融 地理地名 农场牧场 公共厕所
数量/个 204 1787 1028 364 356 2705 1177 181

3 实验与可视化方法

本文实验主要包括核密度分析、数据网格化、双因素组合制图等方法,这些方法用于对夜光遥感数据和POI数据的预处理和可视化中。在完成数据分析和可视化之后,将结合实验结果对三亚市夜光遥感和POI数据的耦合关系进行讨论,并分析两种数据在表征城市空间结构中的特点,在此基础上讨论港口在三亚城市空间结构的地位。

3.1 核密度分析

空间上的任何位置都可能出现各类地理事件,且不同位置上事件发生的概率不相同,核密度分析就是一种表征地理事件在某一区域发生概率的方法[13]。核密度分析可以对点数据进行高质量的密度估计,且不会受到栅格大小和位置的影响,已经被广泛应用于地理学中的景观格局分析、商业空间布局分析、就业空间布局分析、产业空间分布等研究,是一种对POI数据进行概率密度估计的有效手段。
对于空间区域中任一点S,其核密度估计值是其邻域内所有点的核函数对点S的贡献之和。整个空间内点S的核密度估计计算公式如式（1）所示。
f S = 1 h ∑ i = 1 n K D S , S i h （1）
式中：n表示距离阈值范围内包含的空间实体数量;K（ ）表示核函数,一般使用四次多项式核函数;h表示距离阈值（带宽）,表示两点间的欧式距离[16]。
由式（1）及相关研究表明,带宽h的选择对核密度分析的结果有关键影响。对于POI数据的核密度分析来说,带宽需要根据POI数据的实际空间分布以及研究问题合理确定,较小的带宽适合反映密度分布的局部变化,较大的带宽则能有效地反映全局尺度的空间变化。
本文在考虑夜光遥感的空间分辨率以及多次实验之后,选取1500 m作为POI核密度分析的带宽,空间分辨率设置为200 m,最终得到三亚市POI核密度分析的栅格结果。

3.2 数据网格化

数据网格化是指将点状定位数据通过空间拓扑关系分析转换为面状数据的方法[22]。点与面之间的空间拓扑关系包括相交、被包含、相离3种,其中被包含属于相交的特殊情况。数据网格化依次统计与每个网格相交的点数据的个数,并可以将点数据的属性作为权重,从而得到该网格内点数据的属性值加权和。数据网格化可以简化数据量,并且将不同类型的属性值链接到统一的网格数据中,方便进行数据间的对比分析,从而提高数据分析的效率。而栅格数据可以视为规则的点状数据,因而也可以通过叠加分析将其转化为面状网格数据。
数据网格化使用的网格有正方形、三角形和正六边形等多种形状,由于正六边形网格更接近圆形,而且具有更丰富的拓扑关系,逐渐得到广泛应用[23]。本文选取正六边形网格作为数据网格化的形状,其中网格的面积设置为1 km2。
本文以三亚市行政区域为范围,建立了2106个面积为1 km2的规则正六边形网格。之后将规则网格和处理得到的夜光遥感数据、POI核密度分析结果分别叠加,分别获取每个网格的内夜光遥感强度和POI核密度的平均值,并将2种平均值链接到同一个规则网格数据,便于后续耦合关系分析。

3.3 双因素组合制图

双因素组合制图是一种可以较直观表达2个变量间耦合关系的可视化方法,其图例如图2所示。对于2种不同的影响因素,针对其数值的高低,可以两两结合成4种组合,并且在这些组合之间可以形成中间过渡,结合成更多的组合。为更好地对这些组合进行可视化,选取了2种不同的色相进行组合,形成了如图2的配色方案。不同组合之间既形成了渐变,也有一定的区分度。
Fig. 2 Legend of mapping from double factors

图2 双因素组合制图图例

该方法可以用于人口、族群变化等方面的制图,通过组合的地图图例和色彩的设计,可以直观地了解双因素间的组合关系。在该图例中,数量也可以更换为数量的增减等其他因素,各因素的分级数目也可以增加或减少,根据制图的需求和因素的实际情况修改即可。
本文采用3×3的分级方式,共包含了9种不同的双因素间组合关系,根据不同关系类型的数量分布和空间分布,探讨夜光遥感与POI数据之间的空间耦合关系,并进一步分析其空间差异和三亚市城市空间结构之间的关系。

4 结果与分析

4.1 夜光遥感与POI数据结果对比

将预处理后的2016年三亚市平均夜光遥感数据和规则六边形网格进行叠加分析,并将夜光遥感数据网格化,得到2016年三亚市夜光遥感强度的规则网格图,结果如图3所示。将筛选后的2016年三亚市POI数据进行核密度分析后,利用规则六边形网格将分析结果数据网格化,得到2016年三亚市POI核密度数值的规则网格图,结果如图4所示。
Fig. 3 Regular grid map of nighttime light data value of Sanya in 2016

图3 2016年三亚市夜光遥感值规则网格图

Fig. 4 Regular grid map of POI kernel density value of Sanya in 2016

图4 2016年三亚市POI核密度值规则网格图

由于夜光遥感数据分辨率较低,以及栅格和矢量数据性质上的差异,使得在统计网格内栅格平均值时,部分沿海地区网格未包含栅格的中心点,导致网格的统计值为0。而POI核密度估计值数据分辨率较高,所以未出现该情况。为尊重原始实验结果,保持对比的一致性,在夜光遥感数据的结果中保留了这一部分网格,并注意不使其影响结果分析。
由图3可知,夜光遥感强度的分布总体呈由沿海向内陆逐渐降低的趋势,形成了市中心（天涯区和吉阳区）、崖州区和海棠区3个沿海的高值中心,分别是三亚港、南山港和铁炉港3个港口的所在地。其中,市中心附近夜光遥感高值范围最大,且向内陆较大范围延伸;崖州区驻地附近形成圆形夜光遥感高值聚居区,并向南延伸至南山港附近;海棠区附近则自驻地向南沿海岸伸展至铁炉港附近形成带状夜光遥感高值区域。
由图4可知,POI核密度值的空间分布与夜光遥感强度的空间分布类似,大致呈从沿海向内陆逐渐降低的趋势,并形成了相同的3个沿海高值中心。但也存在一些差异,如在市中心和崖州区驻地之间,POI核密度的高值区域呈连续状;三大高值集中区域的范围也相对较小;在北部内陆区域,POI核密度值形成了多个高值点,而夜光遥感数据则为大范围的低值连续区。

4.2 夜光遥感与POI数据耦合关系分析

4.2.1 总体分布
首先将2016年三亚市夜光遥感数据和POI数据核密度值进行归一化处理,以更好处理二者之间的关系,并按照标准差分级法中三分法中常用的 ±1.5倍标准差为分级界线各分为高、中、低三级。然后,利用双因素组合制图的方法,对2016年三亚市夜光遥感数据和POI数据核密度值的空间耦合关系进行可视化,得到了2016年三亚市夜光遥感与POI数据空间耦合关系图（图5）。
Fig. 5 Spatial coupling map of nighttime light data and POI kernel density of Sanya in 2016

图5 2016年三亚市夜光遥感与POI数据空间耦合关系图

由图5可知,二者的空间耦合关系相同（低-低、中-中、高-高）的区域所占比重最大,达85.6%,且大致呈环状结构由市中心区域向外扩散。其中,耦合关系为高-高的区域集中分布于市中心区域;而在高-高耦合关系的外围以及崖州区和海棠区驻地附近,则分布有中-中耦合关系;再向外的其他广大区域,则几乎均为低-低耦合关系。这说明在多数区域,夜光遥感和POI数据具有相同的空间分布特点,而高-高和中-中耦合关系的区域则为城镇中心区。
4.2.2 POI核密度值低于夜光遥感
除空间耦合关系相近的区域外,其他空间耦合关系的类型也有一定分布,但这些区域数量较少,但对了解2种数据之间的差异,解读城市空间结构具有重要意义。为更好地探究这些区域,本文对这些空间耦合关系的区域单独提取出进行制图。其中POI核密度值低于夜光遥感的区域分布图如图6所示。
Fig. 6 Distribution map of areas with nighttime light data value higher than POI kernel density value

图6 夜光遥感强度值高于POI核密度值区域分布图

由图6可知,夜光遥感强度值高于POI核密度值的区域主要分布于市中心、崖州区驻地和海棠区驻地的外围或附近沿海区域。这些区域中,以低-中和中-高的耦合关系为主,呈环状或带状分布,且中-高耦合关系的区域分布于低-中耦合关系区域的内侧,同时位于城镇中心区的外侧,体现了夜光遥感数据的“溢出”效应[24]。说明这二者所覆盖的区域主要为城镇向农村过度的城市边缘区。
低-高耦合关系的分布区域则较少,在三亚市境内,主要分布于天涯区驻地西北和崖州区驻地以南两处,前者为三亚凤凰国际机场所在地,而后者为经济开发区三亚市创意产业园所在地。这些区域的特征为夜光遥感强度值大幅高于POI核密度值,表明这些区域城市照明设施建设较好,面积较广却POI信息匮乏的独特特征。
夜光遥感可以表征城市夜间灯光的强度,但是区域的具体空间特征则难以表征,如中心城区、经济开发区和机场等无法区分;POI数据由于表达为点状实体,对于经济开发区、机场和新城区等较大范围的同质性区域也以少量点数据表达,形成了对区域空间特征的不合理表征。而通过对夜光遥感强度和POI核密度值的耦合分析,可以更显著地表征出较大范围的同质性区域的空间特征。
4.2.3 POI核密度值高于夜光遥感
对空间耦合关系为POI核密度值高于夜光遥感的区域进行制图,其结果如图7所示。
Fig. 7 Distribution map of areas with nighttime light data value lower than POI kernel density value

图7 夜光遥感强度值低于POI核密度值区域分布图

从图7可看出,夜光遥感强度值低于POI核密度值的区域分布较少,主要散布在中心城区之外的区域。这些区域均呈现小片聚集的形态,且与郊区驻地和原乡镇驻地的空间分布耦合度较高。其中有四个较大区块,分别位于崖州区驻地、海棠区驻地和区划调整前育才镇和高峰乡驻地附近。其中崖州区驻地区域的主要为高-中耦合关系,其余区域则以中-低耦合关系为主,说明崖州区的城镇化水平相对较高。
夜光遥感强度值低于POI核密度值的结果表征了这一区域城镇化建设较为落后但工商业、服务业等聚集的特点。具体来说,由于郊区及市区外的乡镇中心城镇化建设相对落后,其夜光遥感强度值较低,不利于表征其城镇中心的空间特征与城镇体系中的地位;而POI数据则通过对郊区等区域的工商业、服务业信息的记录,较好的表征了城镇中心的空间特点。此外,高-中和中-低等不同的耦合关系也可以表征这些区域之间城镇化的差异性。通过对夜光遥感和POI数据的结合使用,可以更好地表征郊区及市区外的乡镇中心的空间特征。

5 结论

本文通过对2016年三亚市夜光遥感和POI数据的空间耦合关系进行分析,研究了夜光遥感和POI数据的空间特征,对二者之间差异区域的空间分布及其和城市空间结构的关系进行了探讨,并进一步分析了港口在三亚城市空间结构中的地位,得出了如下主要结论：
（1）夜光遥感和POI数据的空间耦合关系较好,具有较高的一致性。2种数据在三亚市的空间分布总体趋势相一致,空间耦合关系相同的区域占比达85.6%,均较好地表征了市中心、崖州区和海棠区的三大城镇化中心区及其空间形态。这说明2种数据在城市空间结构研究中均有较好的适用性。
（2）夜光遥感和POI数据耦合关系相异的区域可以更显著的表征部分城市结构的空间特征,如大范围同质性区域、城市边缘区、郊区和乡镇中心及其城镇化水平等。二者的空间特征具有一定的差异,POI数据在经济开发区、新城区、机场等区域分布较少,而在郊区、乡镇等城镇中心具有一定分布;夜光遥感数据则在城镇化中心区、经济开发区、机场等基础设施建设较好的区域强度较高,并且具有显著的“溢出”效应,但对于乡镇中心则无法表征。
（3）三亚市作为重要的南海港口城市,其城市的空间结构与港口关系紧密。三亚市的三大城镇化中心区与3个主要港口密切相关,市中心围绕三亚港扩展,崖州区在南山港附近形成经济开发区,而海棠区政府驻地与铁炉港之间沿海形成带状城镇区。在中心区之外,主要为广阔的农村区域,并形成了多个乡镇中心。
本研究也存在一定的不足：仅以三亚市为例进行了讨论;对2种数据的空间耦合关系仅限于三亚市内存在的城市空间结构;空间耦合关系的研究也以定性研究为主。今后,拟针对更多的南海港口城市开展研究,更全面地使用量化方法,进一步探究 2种数据的空间耦合关系以及港口在南海港口城市空间结构中的地位。

The authors have declared that no competing interests exist.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
石伟,苏奋振,周成虎,等.南沙岛礁及周边港口可达性评价模型研究[J].地理学报,2014,69(10):1510-1520.

[ Shi W, Su Z F, Zhou C H, et al.Research on accessibility model of Nansha Islands and surrounding seaports[J]. Acta Geographica Sinica, 2014,69(10):1510-1520. ]

[2]
齐庆华,蔡榕硕. 21世纪海上丝绸之路海洋环境的气候变化与风暴灾害风险探析[J].海洋开发与管理,2017,34(5):67-75.

[ Qi Q H, Cai R S.The climate changes of marine environment and storm risk on the 21st century maritime silk road[J]. Ocean Development and Management, 2017,34(5):67-75. ]

[3]
朱国强,苏奋振,张君珏.南海周边国家近20年海岸线时空变化分析[J].海洋通报,2015,34(5):481-490.

[ Zhu G Q, Su F Z, Zhang J Y.Analysis on spatial-temporal changes of the coastline in the countries around the South China Sea in recent two decades[J]. Marine Science Bulletin, 2015,34(5):481-490. ]

[4]
胡文秋,苏奋振,王武霞,等.越南下龙市不同时期土地利用变化特征[J].地球信息科学学报,2017,19(4):570-579.在RS与GIS技术支持下,采用人机交互影像解译方法获取了1973、1988、2003和2014年4期土地利用数据,对越南建国到革新开放、革新开放至施行社会主义市场经济以及社会主义市场经济体制改革至今3个不同发展时期下龙市土地利用方式进行了定量研究,并从土地利用变化速度、程度及转移方向3个方面分析了过去40年该区域土地利用格局及时空变化特征。结果表明：（1）建国至今下龙市林地占比缩减了26.3%,城镇用地与工矿仓储二者占比扩大了4.3倍,耕地和红树林仅占下龙市总面积的3.6%和1.3%,同时养殖用地占比为5.5%。（2）建国初期至革新开放时期,下龙市土地利用变化主要是城镇用地和工矿仓储的增建,二者均上升4%左右,空间变化集中于鸿基区,林地减少8.1%,耕地减少3.2%,土地利用总格局未产生明显变化。（3）革新开放至施行社会主义市场经济时期,城镇用地和工矿仓储扩建程度分别为过去15年的3倍和2倍,鸿基和拜寨的城区建设逐渐趋于平衡,林地占比减至52.2%,新地类养殖用地出现,红树林锐减。土地利用空间格局呈现破碎化。（4）施行社会主义市场经济体制到至今,林地锐减至40.0%,土地利用格局由林地占主导地位变为人工地类占主导,耕地和养殖用地分布边缘化。

[ Hu W Q, Su Z F, Wang W X, et al.The characteristics of land use change during the different periods in Halong city[J]. Journal of Geo-information Science, 2017,19(4):570-579. ]

[5]
张虎. 南海区域港口国监督合作机制探析[J].海南大学学报(人文社会科学版),2014,32(6):59-66.

[ Zhang H.An analysis of the cooperative mechanism of port state control in the South Sea[J]. Humanities ＆ Social Sciences Journal of Hainan University, 2014,32(6):59-66. ]

[6]
李德仁,李熙.论夜光遥感数据挖掘[J].测绘学报,2015,44(6):591-601.lt;p>如果从地球上空观测夜间的地球,可以发现人类聚居区和经济带发出夺目的光芒。当夜间的天空无云时,遥感卫星能够捕捉到城镇灯光、渔船灯光、火点等可见光辐射源,这些夜间无云条件下获取的地球可见光的影像即夜光遥感影像。与日间遥感不同,夜光遥感对于反映人类社会活动具有独特的能力,因此被广泛应用于社会经济领域的空间数据挖掘。本文首先介绍能够观测夜间灯光的卫星遥感观测平台和传感器, 然后从社会经济参数估算、城市化监测与评估、重大事件评估、环境及健康效应研究、渔业信息提取、流行病研究、油气田监测等方面总结了夜光遥感数据挖掘的现状和特点。最后,文章从新型数据源、知识发现、地面观测和地理国情&mdash;世情监测4个方面提出了夜光遥感及其数据挖掘的未来发展趋势。</p>

DOI

[ Li D R, Li X.An overview on data mining of nighttime light remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2015,44(6):591-601. ]

[7]
王利伟,冯长春.转型期京津冀城市群空间扩展格局及其动力机制——基于夜间灯光数据方法[J].地理学报,2016,71(12):2155-2169.针对中西方关于城市群空间扩展路径研究的争论,以京津冀城市群为例,运用夜间灯光数据,采取扩展强度指数、空间关联模型、多维驱动力分析模型,定量揭示了城市群时空扩展路径及其动力机制。结果表明：1 1992-2012年京津冀城市群时空扩展呈现以京津唐为核心的中心集聚扩展模式,保定—衡水—石家庄之间的＂三角地带＂成为城市群空间扩展的冷点区;2城市群空间扩展虽然已经出现了扩散势头,但向心集聚的惯性作用力依然强大;3城市群时空扩展的动力机制表现出以市场力为主要驱动因子的特征,行政力、外向力和内源力对城市群空间扩展的影响作用依次递减,从城市群空间扩展的驱动力演化趋势看,市场力、行政力、外向力的影响呈现上升趋势,而内源力的影响系数则呈现下降趋势。最后,提出降低行政干预、构建市场主导机制、强化内外双向开放、推动产业升级的政策建议,促进城市群空间结构持续优化。

DOI

[ Wang L W, Feng C C.Spatial expansion pattern and its driving dynamics of Beijing-Tianjin-Hebei metropolitan region: Based on nighttime light data[J]. Acta Geographica Sinica, 2016,71(12):2155-2169. ]

[8]
Xie Y, Weng Q.Updating urban extents with nighttime light imagery by using an object-based thresholding method[J]. Remote Sensing of Environment, 2016,187:1-13.61An object-based method to optimize thresholds of urban objects from NTL imagery61An object-based normalization for inter-calibrating nighttime light imagery61Estimated thresholds correlated with the reference withR2>0.96 and RMSE<4.161Derived urban maps yielded an OA>94% and Kappa>0.53 at the city scale.61The NTL-OUT method shows great potentials to update large-scale urban extents.

DOI

[9]
王旭,吴吉东,王海,等.基于夜间灯光和人口密度数据的京津冀GDP空间化对比[J].地球信息科学学报,2016,18(7):969-976.lt;p>国内生产总值（GDP）是衡量地区经济发展水平的重要指标,GDP的空间化可以为灾害风险分析等多学科交叉研究提供基础数据。空间化代用数据的选择是社会经济统计数据空间化的关键,本文以京津冀地区作为研究区,将夜间灯光、全球人口密度（LandScan）和亚洲人口密度（AsiaPop）空间分布信息作为代用数据,将市级GDP统计数据空间展布到栅格单元,以绝对误差、相对误差和均方根误差为指标,利用县级统计数据对展布结果进行误差分析,并对比3种数据对GDP空间模拟的表达效果。结果表明：相对于夜间灯光和LandScan数据,AsiaPop模拟得到的综合误差最小;基于夜间灯光和LandScan的GDP空间展布误差格局比较接近,即存在经济较发达的市辖区GDP值被低估、市郊区县GDP被高估的误差“两极区”倾向,而基于AsiaPop的GDP空间展布误差格局与经济发展水平关系不密切。因此,利用单一代用数据很难合理地反映经济活动的空间分布,综合夜间灯光、人口密度、道路和建筑物等多源空间数据是提高GDP空间展布精度的发展趋势。</p>

DOI

[ Wang X, Wu J D, Wang H, et al.Comparison of GDP spatialization in Beijing-Tianjin-Hebei based on night light and population density data[J]. Journal of Geo-information Science, 2016,18(7):969-976. ]

[10]
Chen Y, Liu X, Li X.Analyzing parcel-level relationships between urban land expansion and activity changes by integrating landsat and nighttime light data[J]. Remote Sensing, 2017,9(2):164.

DOI

[11]
李德仁,余涵若,李熙.基于夜光遥感影像的“一带一路”沿线国家城市发展时空格局分析[J].武汉大学学报·信息科学版,2017,42(6):711-720.

[ Li D R, Yu H R, Li X.The spatial-temporal pattern analysis of city development in countries along the belt and road initiative based on nighttime light data[J]. Geomatics and Information Science of Wuhan University, 2017,42(6):711-720. ]

[12]
吴健生,刘浩,彭建,等.中国城市体系等级结构及其空间格局——基于DMSP/OLS夜间灯光数据的实证[J].地理学报,2014,69(6):759-770.A perfect urban system can promote the urban agglomeration effect, and the prerequisite for urban system is that regional socio-economic development should actually reach a certain stage. Thanks to the strong correlation between the urban element flow and the mass of spatial units, the node system study in general should scientifically measure the interaction intensity among spatial units, and also avoid the difficulty that the vector data of the function system study is still hard to obtain and measure. As a special node system study data, DMSP/OLS nighttime light data can be used to measure urban human's activity intensity and activity breadth comprehensively, having a strong positive correlation with local GDP, population size, urbanization level and so on. So the urban nighttime light index can make a perfect combination between a single statistical index and a comprehensive index system. The gravity model based on DMSP/OLS nighttime light data is used to measure the interaction intensity among 341 cities in mainland China, and then the two step cluster analysis is made to analyze hierarchical structure and spatial pattern of mainland China urban system comprehensively. There are 7 national node cities (including Beijing, Tianjin, Shanghai, Guangzhou, Chongqing, Harbin and Shenyang), 26 regional node cities and 107 provincial node cities in mainland China. Mainland China urban system are divided into 2 national urban systems (the northern national urban system and the southern national urban system) and 8 regional urban systems (North China urban system, Northeast China urban system, Northwest China urban system, Middle Yellow River urban system, and East China urban system, South China urban system, Southwest China urban system, Middle Yangtze River urban system), as well 31 provincial urban systems. Every urban system is closely linked, interdependently and interactionally, whose slope is relative and local area, and then some urban systems and many cities may be define accurately again, but the empirical study of hierarchical structure and spatial pattern of mainland China urban system evidenced from DMSP/OLS nightlight data is rather objective and compellent. All in all, to analyze hierarchical structure and spatial pattern of urban system based on DMSP/OLS nightlight data is obviously feasible and scientific.

DOI

[ Wu J S, Liu H, Peng J, et al.Hierarchical structure and spatial pattern of China's urban system: Evidence from DMSP/OLS nightlight data[J]. Acta Geographica Sinica, 2014,69(6):759-770. ]

[13]
许泽宁,高晓路.基于电子地图兴趣点的城市建成区边界识别方法[J].地理学报,2016,71(6):928-939.城市建成区边界是认识和研究城市的重要基础性信息,也是落实城市功能空间布局、实施界限管控的前提。但是,以往通过夜间灯光的强度、土地覆被或建筑覆盖率等信息获取城市空间范围的方法,由于受到数据精度和尺度限制,对城市社会经济活动的解释性不强,因而存在较大局限性。电子地图兴趣点(POI)作为城市空间分析的基础数据之一,直观且有效地反映了各类城市要素的集聚状况。本文基于POI与城市空间结构和城市要素空间分布的关联性,提出了一种新的通过POI密度分布来判别城市建成区边界的技术方法。为此,开发了DensiGraph分析方法,用来分析POI密度等值线的变化趋势,在此基础上对城乡过渡地带的阈值识别的方法进行了理论分析,并讨论了单中心圆结构、双中心"鱼眼型"结构、双中心"子母型"结构等各类城市POI密度等值线的生长规律,证明了Densi-Graph分析方法的适用性。较之以往的城市建成区边界识别方法,这种方法的基础数据更加直观可信,分析结果也更加客观。运用这种方法,本文对全国地级以上城市的建成区边界进行了实证分析,探索了城市建成区边界的阈值及其与城市人口规模、城市所在区域之间的关系。

DOI

[ Xu Z N, Gao X L.A novel method for identifying the boundary of urban built-up areas with POI data[J]. Acta Geographica Sinica, 2016,71(6):928-939. ]

[14]
Li M, Shen Z, Hao X.Revealing the relationship between spatio-temporal distribution of population and urban function with social media data[J]. Geojournal, 2016,81(6):1-17.ABSTRACT This is the original version, not the published version.

DOI

[15]
Jiang S, Alves A, Rodrigues F, et al.Mining point-of-interest data from social networks for urban land use classification and disaggregation[J]. Computers Environment & Urban Systems, 2015,53:36-46.Over the last few years, much online volunteered geographic information (VGI) has emerged and has been increasingly analyzed to understand places and cities, as well as human mobility and activity. However, there are concerns about the quality and usability of such VGI. In this study, we demonstrate a complete process that comprises the collection, unification, classification and validation of a type of VGI—online point-of-interest (POI) data—and develop methods to utilize such POI data to estimate disaggregated land use (i.e., employment size by category) at a very high spatial resolution (census block level) using part of the Boston metropolitan area as an example. With recent advances in activity-based land use, transportation, and environment (LUTE) models, such disaggregated land use data become important to allow LUTE models to analyze and simulate a person’s choices of work location and activity destinations and to understand policy impacts on future cities. These data can also be used as alternatives to explore economic activities at the local level, especially as government-published census-based disaggregated employment data have become less available in the recent decade. Our new approach provides opportunities for cities to estimate land use at high resolution with low cost by utilizing VGI while ensuring its quality with a certain accuracy threshold. The automatic classification of POI can also be utilized for other types of analyses on cities.

DOI

[16]
李国旗,金凤君,陈娱,等.基于POI的北京物流业区位特征与分异机制[J].地理学报,2017,72(6):1091-1103.物流节点和物流企业作为物流空间的核心载体和组织主体,其区位特征和分异机制识别对优化城市物流空间布局、合理配置物流资源有重要意义。2014年12月基于腾讯在线地图平台,采集了北京市4396个物流POI。采用产业集中度评价、核密度分析等方法,刻画了北京物流空间格局,阐明了类型差异、供需侧因素与区位选择行为的微观作用机理,揭示了交通、地租、资产对空间分异形成的内在机制。研究表明：① 物流企业和物流节点呈现协同集聚和空间分离相结合的区位特征;物流活动总体呈现“中心边缘、近郊和远郊交错”的空间格局,与物流就业空间耦合度低。② 由物流园区和物流中心构成的公共物流空间是政府引导的结果,由服务于特定行业和终端用户的配送中心构成的末端物流空间多为企业主导,两者区位分异显著。③ 在物流区位形成过程中,政府通过规划交通线路和货运场站改变交通区位条件,配置物流仓储用地影响不同区域物流地租和可得性,进而调控企业行为并形成物流空间的类型与职能分异;企业则通过资产配置的差异化来满足不同服务对象的多样化需求,促进专业化分工并形成物流空间的对象分异。

DOI

[ Li G Q, Jin F J, Chen Y, et al.Location characteristics and differentiation mechanism of logistics industry based on points of interest: A case study of Beijing[J]. Acta Geographica Sinica, 2017,72(6):1091-1103. ]

[17]
李江苏,梁燕,王晓蕊.基于POI数据的郑东新区服务业空间聚类研究[J].地理研究,2018,37(1):145-157.

[ Li J S, Liang Y, Wang X R.Spatial clustering analysis of service industries in Zhengdong New District based on POI data[J]. Geographical Research, 2018,37(1):145-157. ]

[18]
Yue Y, Zhuang Y, Yeh A G O, et al. Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy[J]. International Journal of Geographical Information Systems, 2017,31(4):658-675.Although mixed use is an emerging strategy that has been widely accepted in urban planning for promoting neighbourhood vibrancy, there is no consensus on how to quantitatively measure the mix and the effects of mixed use on neighbourhood vibrancy. Shannon entropy, the most commonly used diversity measurement in assessing mixed use, has been found to be inadequate in measuring the multifaceted, multidimensional characteristics of mixed use. And lack of data also makes it difficult to find the relationship between mixed use and neighbourhood vibrancy. However, the recent availability of new sources including mobile phone data and Point of Interest (POI) data have made it possible to develop new indices of mixed use and neighbourhood vibrancy to analyse their relationships. Taking advantage of these emerging new data sources, this study used the numbers of mobile phone users in a 24-hour period as a proxy of neighbourhood vibrancy and used POIs from a navigation database to develop a series of mixed-use indicators that can better reflect the multifaceted, multidimensional characteristics of mixed-use neighbourhoods. The Hill numbers, a unified form of diversity measurement used in ecological literature that includes richness, entropy, and the Simpson index, are used to measure the degrees of mixed use. Using such fine-grained data sets and the Hill numbers allowed us to obtain better insights into the relationship between mixed use and neighbourhood vibrancy. Four models varying in POI measurements that reflect different dimensions of mixed use were presented. The results showed that either POI density or entropy can explain approximately 1% of neighbourhood vibrancy, while POI richness contributes significantly in improving neighbourhood vibrancy. The results also revealed that the entropy has limitations as a measure for representing mixed use and demonstrated the necessity of adopting a set of more appropriate measurements for mixed use. Increasing the number of POIs has limited power to improve neighbourhood vibrancy compared with encouraging the mixing of complementary POIs. These exploratory findings may be useful for adjusting mixed-use assessments and to help guide urban planning and neighbourhood design.

DOI

[19]
Jin X, Long Y, Sun W, et al.Evaluating cities' vitality and identifying ghost cities in China with emerging geographical data[J]. Cities, 2017,63:98-109.With the rapid urbanization of China, plenty of new urban lands have been developed with the great expectation to deal with all kinds of issues in old urban areas such as high population density, great demand on limited land resources, and decaying environment. However, a great proportion of vacancy in these newly developed units leads to the undesired observation of ghost cities. Lacking of clear and effectively evaluation criterion, the understanding of ghost cities in China is then rather limited. Considering the fact of ghost cities, we borrow the theory of urban vitality to identify and evaluate ghost cities in this paper. We argue that ghost cities are associated with very low urban vitality. In the light of big/open data, we are able to profile ghost cities of China based on 535,523 recent project-level residential developments from 2002 to 2013. We use the national-wide and million magnitude road junctions, points of interest and location based service records of 2014/2015 for measuring the morphological, functional and social vitality of each residential project. We then aggregate the project level evaluation results into the city level and thirty ghost cities are then identified by comparing the residential projects' vitality in the old (developed before or in 2000) and new (developed after 2000) urban areas in each city. Our profiling results illustrate the big picture of China's past residential developments, and then of ghost cities. We find the average vitality of residential projects in new urban areas is only 8.8% of that in old urban areas, denoting the potential existence of ghost cities in newly developed areas in Chinese cities. We have also benchmarked our identified ghost cities with existing rankings, the Baidu searching engine and night-time light images. Although we admit that ghost cities may exist in the particular urbanizing phase of China and that some ghost cities now may be well developed in the future, this study provides a thorough evaluation on the ghost city condition in China. This may shed light on policy implications for Chinese urban development.

DOI

[20]
潘思东. 基于夜光遥感和小区POI的住宅发展与经济增长的空间耦合研究[J].地球信息科学学报,2017,19(5):646-652.针对城市住宅业发展与其经济增长之间的单向或双向因果关系问题,有关学者利用宏观统计分析的方法得到了不同的结论。本文通过构建二者的时空数据集,在城市内部微观层面上剖析了二者的耦合联系及其空间差异性,以期在细尺度上解释二者之间的关系。本文选取郑州市作为研究区,提出了一种基于夜光遥感数据的GDP空间化估算方法,进而生成GDP时空数据集;基于住宅小区POI点数据对城市住宅进行空间密度估计,得到住宅小区的时空分布数据集;最后对GDP和住宅建设密度进行了空间互相关分析,探究住宅发展与经济增长像元尺度上的共变趋势。结果表明:与前人的宏观研究论断不同,耦合分析结果显示住宅业发展与经济增长之间的关系在城市内部具有空间差异性,两者既存在相互影响的区域,也存在无相关的区域;耦合协调关系极显著的区域约占两成,且主要位于市属区和县域中心区;耦合不显著和不相关的区域超过七成,大部分位于市属县域。

[ Pan S D.Spatial coupling between housing development and economic growth based on night light remote sensing and residential POI[J]. Journal of Geo-information Science, 2017,19(5):646-652. ]

[21]
Wang R, Wan B, Guo Q, et al.Mapping regional urban extent using NPP-VIIRS DNB and MODIS NDVI data[J]. Remote Sensing, 2017,9(8):862.The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer鈥檚 accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.

DOI

[22]
于丙辰,陈刚.基于腾讯区域热力图的庐山核心景区客流研究[J].国土与自然资源研究,2017,39(2):83-89.

[ Yu B C, Chen G.Research on visitor flows of Lushan core scenic area using tencent regional heatmap[J]. Territory & Natural Resources Study, 2017,39(2):83-89. ]

[23]
Gao S, Janowicz K, Montello D R, et al.A data-synthesis-driven method for detecting and extracting vague cognitive regions[J]. International Journal of Geographical Information Systems, 2017,31(6):1245-1271.Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. The exact delineation of cognitive regions is challenging insofar as borders are vague, membership within the regions varies non-monotonically, and raters cannot be assumed to assess membership consistently and homogeneously. In a study published in this journal in 2014, researchers devised a novel grid-based task in which participants rated the membership of individual cells in a given region and contrasted this approach to a standard boundary-drawing task. Specifically, the authors assessed the vague cognitive regions of Northern California and Southern California. The boundary between these cognitive regions was found to have variable width, and region membership peaked not at the most northern or southern cells but at substantially less extreme latitudes. The authors thus concluded that region membership is about attitude, not just latitude. In the present work, we reproduce this study by approaching it from a computational fourth-paradigm perspective, i.e., by the synthesis of high volumes of heterogeneous data from various sources. We compare the regions which we identify to those from the human-participants study of 2014, identifying differences and commonalities. Our results show a significant positive correlation to those in the original study. Beyond the extracted regions themselves, we compare and contrast the empirical and analytical approaches of these two methods, one a conventional human-participants study and the other an application of increasingly popular data-synthesis-driven research methods in GIScience.

DOI

[24]
赵敏,程维明.基于DMSP/OLS夜间灯光数据的城市空间扩展研究综述[J].测绘与空间地理信息,2015,38(3):64-68.不同于传统的遥感探测器，DMSP／OLS可获取居民地、车流、渔船等夜间灯光信号，被广泛地应用于城市空间扩展研究中。目前，基于灯光数据的城市扩展研究很多，但缺乏对该领域研究进展的系统性分析与总结。基于此，分析比较现有研究实例，从阈值设定法提取城区和灯光数据城市监测两方面展开讨论，分析目前研究欠缺之处并探讨解决方案。同时，构建新型城市重心模型，进行实例分析，最后展望夜间灯光数据在城市空间扩展方面的研究趋势：①加强对数据本身处理方法的研究；②充分利用灯光强度信息；③构建新型指标反映城市扩展。

DOI

[ Zhao M, Cheng W M.Overview of researches based on urban expansion Via DMSP/OLS night -time light data[J]. Geomatics & Spatial Information Technology, 2015,38(3):64-68. ]

Options
Outlines

/

〈 〉