Reconstruction and Pattern Analysis of Historical Urbanization of Pre

sustainability

Article Reconstruction and Pattern Analysis of Historical Urbanization of Pre-Modern China in the 1910s Using Topographic Maps and the GIS-ESDA Model: A Case Study in Zhejiang Province, China

Zhiwei Wan 1 , Xi Chen 1, Min Ju 1, Chaohao Ling 2, Guangxu Liu 3 , Fuqiang Liao 1,*, Yulian Jia 1,4 and Meixin Jiang 1

1 Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of Education, School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China; [email protected] (Z.W.); [email protected] (X.C.); [email protected] (M.J.); [email protected] (Y.J.); [email protected] (M.J.) 2 State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; [email protected] 3 School of Geography and Environmental Engineering, Gannan Normal University, Ganzhou 341000, China; [email protected] 4 Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, Linyi University, Linyi 276000, China * Correspondence: [email protected]

Received: 19 December 2019; Accepted: 9 January 2020; Published: 10 January 2020

Abstract: The pattern of urban land use and the level of urbanization in China’s pre-modernization period are of great significance for land use and land cover change (LUCC) research. The purpose of this study is to construct a 1910s spatial dataset of provincial land urbanization in pre-modern China. Using historical topographic maps, this study quantitatively reconstructs the built-up area of various cities in Zhejiang Province in the 1910s. The research indicates that: (1) During the early period of the Republic of China, there were a total of 252 cities and towns in Zhejiang Province, including 75 cities at or above the county level, 21 acropolis, and 156 towns. The total built-up area was 140.590 km2. (2) The county-level urbanization level had significant agglomeration characteristics. The overall urbanization rate of land was 0.135%. (3) Hot spots analysis showed that the Hang-Jia-Hu-Shao plain is hot spot. (4) The correlation coefficient between the city wall perimeter data recorded in the local chronicles and the measured city wall perimeter was 0.908. The research showed that the military topographic maps possessed a good application prospect for the reconstruction of urbanization levels. The research results provide direct evidence for urbanization and urban land use in China’s pre-modernization period.

Keywords: urbanization level; historical land use; pre-modern China; Zhejiang Province

1. Introduction Land use and land cover change (LUCC) is one of the primary ways human activities aﬀect the earth system [1], and it has complex regional and global ecological environmental impacts and responses. Among the various land cover changes, urbanized areas are the most severe form. Urbanization is the process of transforming agriculturally oriented areas into non-agricultural forms by changing the land use structures [2,3]. Unreasonable urbanization may cause serious ecological and environmental problems [4], such as the reduction of forests [5] and arable land [6], and the destruction of the rivers

Sustainability 2020, 12, 537; doi:10.3390/su12020537 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 537 2 of 18 network [7] and wetlands [8]. These are all issues that must be faced in sustainable development; long-term sequences of urban land use processes can provide basic data for this research [9]. The urbanization process is an important indicator that reflects regional development. In the past 100 years, global urbanization has developed rapidly. The world urbanization level rose from 16.4% in 1900 to 46.8% in 2000 [10]. According to United Nations forecasts, 68% of the world population will live in urban areas by 2050 [11,12]. Since World War II, with the economic development, the level of urbanization in many developing countries has increased significantly. The urbanization level in India increased from 17% in 1950 to 32% in 2015 and in Brazil it increased from 47% in 1950 to 82% in 2015 [13]. Among the major industrialized large countries, the urbanization level in Russia increased from 44% in 1950 to 74% in 2015 and in the United States it increased from 74% in 1950 to 81% in 2015 [14]. With the rapid development of China’s society and economy after the reform and opening, China’s urbanization level also increased from 18% in 1978 to nearly 60% in 2018 [15,16]. Many scholars have begun to explore the historical background of this phenomenon from the viewpoint of the evolution of Chinese urban development [17]. Relevant studies [18,19] have shown that, in addition to military and political factors, the development of cities and towns during the historical period was closely related to the development of commercial industries. In particular, the rise of commercial towns in the Jiangnan region, which primarily includes Jiangsu and Zhejiang Provinces, since the period of the Republic of China (1911–1949), has played an important role in pre-industrial urbanization [19,20]. Due to missing related statistics, some scholars have estimated the level of urbanization during Chinese historical periods. For example, G. William Skinner [21,22] estimated that the level of urbanization in the Jiangnan region was approximately 10% at the end of the 19th century. Chen [23] used survey data from the government of the Republic of China to study the level of urbanization of the population in Jiangnan. The results showed that the level of urbanization during the 1930s was approximately 15%. Since most of these studies are speculative analyses, further quantitative analyses are yet to be conducted. In addition, some scholars have begun to try to reconstruct the spatial patterns of urban and rural construction land during the middle of the Qing Dynasty (1644–1911) based on an allocation model under the control of statistical materials [19]. Some analyses showed that [24,25], based on the allocation model, only the potential land use pattern could be obtained, not the actual land cover. Therefore, more direct data are needed for the reconstruction of historical urban land spatial patterns. Remote sensing image data are generally only approximately 40 years old [26,27]. Hence, other types of data sources are urgently needed to reconstruct the urbanization level and spatial patterns of the past 100 years and during pre-modern China. Historical military topographic maps and old maps play a very important role in land use reconstruction [28], urbanization development [29], and ecological assessment [30,31]. Combined with GIS and remote sensing information [32], historical topographic maps can provide different kind of historical land use change information for decades to over 200 years [28,33,34], such as the geomorphological evolution of the plain [35], archaeological survey [36], landscape and land use changes [37–39], and hydrological environment [29,40]. In recent years, with the exploitation of a series of historical military topographic map data, topographic maps that were completed in the early 20th century are employed to be crucial data sources for historical urban land use reconstruction in China [41–43]. Studies have shown that [44,45] military topographic maps during the period of the Republic of China were completed based on modern surveying and mapping techniques, and their errors were generally between 1/4000 and 1/1000. In addition, because urban areas have important military value, this type of topographic map has a more detailed mapping of towns and surrounding areas [46]. During this period, the military topographic maps marked urban areas in accordance with the unified standard [47] (Figure1). In particular, urban areas were drawn with city walls or color blocks, which can be identified easily and used for urban land reconstruction. In this study, the urbanized areas above the town level in Zhejiang Province (Figure2), which belongs to a core area of Jiangnan, is utilized as the research object. Then, according to the military topographic map data Sustainability 2020, 12, 537 3 of 18 SustainabilitySustainability 20202020,, 1212,, 537537 33 ofof 2020 producedproduced during during the the Republic Republic of of China, China, an an exploratory exploratory spatial spatial data data analysis analysis (ESDA) (ESDA) in in the the ArcGIS ArcGIS platformplatform is is used used to to perform perform a a spatialspatial analysis analysis and and area area calculation.calculation. The The research research results results can can provide provide historicallyhistorically basicbasicbasic data datadata for for thefor studythethe studystudy of urbanization ofof urbanizationurbanization in pre-modern inin prepre China.‐‐modernmodern In addition,China.China. InIn the addition,addition, urbanization thethe urbanizationdataurbanization series can datadata be extended seriesseries cancan by bebe approximately extendedextended byby approximatelyapproximately a century. aa century.century.

FigureFigure 1. 1. SystematicSystematic illustration illustration of of the the ZhejiangZhejiang militarymilitary topographictopographic mapsmaps (1:50,000) (1:50000)(1:50000) in in the the Republic Republic ofof China. China. ( (aa)) Hangxian County County (1913); (1913); ( (bb)) Cixi County (1916); (1916);(1916); ( ((cc))) Xianju Xianju County County (1920); (1920); ( (dd)) Guanhai Guanhai AcropolisAcropolis (1916);(1916); andand ((ee)) LiangtongLiangtong TownTown (1916). (1916).(1916).

Figure 2. Location and topographic maps of Zhejiang Province. FigureFigure 2.2. LocationLocation andand topographictopographic mapsmaps ofof ZhejiangZhejiang Province.Province. 2. Study Area, Materials, and Methods 2.2. StudyStudy Area,Area, Materials,Materials, andand MethodsMethods 2.1. Study Area 2.1.2.1. StudyStudy AreaArea The study area of this article is Zhejiang Province (27◦020–31◦110 N, 118◦010–123◦100 E), which is The study area of this article is Zhejiang Province (27°02′–31°11′ N, 118°01′–123°10′ E), which is locatedThe in study the southeast area of this coastal article area is ofZhejiang China. Province The total (27°02 area is′–31°11 105,500′ N, km 118°012 and′ the–123°10 terrain′ E), is which sloping is 2 locatedlocated inin thethe southeastsoutheast coastalcoastal areaarea ofof China.China. TheThe totaltotal areaarea isis 105,500105,500 kmkm2 andand thethe terrainterrain isis slopingsloping fromfrom southwestsouthwest toto northeast,northeast, withwith variousvarious landformlandform types.types. ZhejiangZhejiang ProvinceProvince isis locatedlocated inin thethe middlemiddle ofof thethe subtropicalsubtropical zonezone andand belongsbelongs toto aa monsoonmonsoon humidhumid climateclimate withwith superiorsuperior naturalnatural conditionsconditions Sustainability 2020, 12, 537 4 of 18

Sustainability 2020, 12, 537 4 of 20 from southwest to northeast, with various landform types. Zhejiang Province is located in the middle of[48]. the The subtropical civilization zone of and this belongs area has to been a monsoon developed humid since climate ancient with times superior and belongs natural to conditions the traditional [48]. Thedeveloped civilization area ofin thisChina area [49]. has been developed since ancient times and belongs to the traditional developedIn general, area in cities China are [49 large]. settlements formed by the aggregation of non‐agricultural industries andIn non general,‐agricultural cities are population, large settlements including formed both by small the aggregation towns and oflarge non-agricultural cities [50]. According industries to and the non-agriculturalChinese traditional population, city classification including both criteria small [18], towns it can and be large divided cities [50into]. Accordinga provincial to thecapital Chinese city, traditionalprefecture city‐level classification cities, county criteria‐level [18 ],cities,it can and be divided other towns. into a provincialZhejiang Province capital city, is prefecture-leveldivided into 12 cities,prefectures county-level (Figure cities, 3), and and each other prefecture towns. Zhejiang is divided Province into isseveral divided counties. into 12 The prefectures political (Figure center3 ),of andZhejiang each prefectureProvince is is Hangzhou divided into Fu, severalwhich belongs counties. to the The prefecture political centerlevel, and of Zhejiang the provincial Province capital is Hangzhoucity is Hangxian Fu, which county. belongs Other to theprefectures prefecture include level, Jiaxing, and the Huzhou, provincial Jinhua, capital Quzhou, city is Hangxian Yanzhou, county.Ningbo, Other Shaoxing, prefectures Wenzhou, include Taizhou, Jiaxing, Huzhou,Chuzhou, Jinhua, and Quzhou,Dinghai. Yanzhou,There are Ningbo, 75 county Shaoxing,‐level Wenzhou,administrative Taizhou, units, Chuzhou, and the andscope Dinghai. of jurisdiction There are is basically 75 county-level the same administrative as that of modern units, Zhejiang and the scopeProvince. of jurisdiction The boundaries is basically of the the administrative same as that ofunits modern at the Zhejiang prefecture Province. and county The levels boundaries were ofdrawn the administrativebased on the CHGIS units at‐1911 the prefecture dataset jointly and county developed levels by were Fudan drawn University based on and the CHGIS-1911 Harvard University dataset jointly[51]. Except developed for cities by Fudan above University the county and level, Harvard the remaining University urbanized [51]. Except areas for are cities mainly above towns, the county which level,provided the remaining commercial urbanized services areasto rural are areas. mainly It is towns, worth which noting provided that there commercial is a special servicestype of acropolis to rural areas.in the It town is worth of Zhejiang noting thatProvince. there isThis a special type of type acropolis of acropolis was originally in the town a military of Zhejiang castle, Province. and gradually This typedeveloped of acropolis into a was commercial originally town a military [52]. castle, and gradually developed into a commercial town [52].

Figure 3. Administrative division map of Zhejiang Province in 1911. Figure 3. Administrative division map of Zhejiang Province in 1911. 2.2. Military Topographic Map 2.2. Military Topographic Map The historical maps used in this study were primarily the military topographic maps of Zhejiang ProvinceThe during historical the maps period used of thein this Republic study were of China primarily archived the military in the Key topographic Laboratory maps of the of Zhejiang Poyang LakeProvince Wetland during and the Watershed period of Research the Republic Ministry of China of Education, archived Jiangxi in the NormalKey Laboratory University. of the This Poyang set of topographicLake Wetland maps and primarily Watershed consists Research of large-scale Ministry of maps Education, of 1:50,000. Jiangxi The Normal mapping University. time was This from set the of Republictopographic of China maps year primarily two (AD consists 1913) of to large the Republic‐scale maps of China of 1:50,000. year 25 The (AD mapping 1936), covering time was the from entire the provinceRepublic of of Zhejiang China year and two including (AD 1913) parts to of the the Republic Jiangxi, of Anhui, China and year Jiangsu 25 (AD Provinces. 1936), covering The surveying the entire andprovince mapping of Zhejiang organization and including was the Zhejiang parts of Armythe Jiangxi, Survey Anhui, Bureau and during Jiangsu the Provinces. early stage. The It surveying was later changedand mapping to the organization Zhejiang Land was Survey the Zhejiang Bureau Army of the Survey Staﬀ Headquarters Bureau during of the the Nationalearly stage. Government. It was later Therechanged are 345to the maps Zhejiang of the wholeLand Survey province, Bureau and the of the size Staff of each Headquarters picture is approximately of the National46 Government. cm 35 cm. × TheThere longitude are 345 span maps of of each the map whole is 15province,0, and the and latitude the size span of iseach 100 .picture The elevation is approximately system assumes 46 cm that × 35 thecm. altitude The longitude of the Ziwei span of Garden each map in the is old15′, provincialand the latitude Administration span is 10′. ofThe Zhejiang elevation is 50system m. The assumes map schemathat the adopts altitude the of original the Ziwei topographic Garden mapin the of old the provincial Republic of Administration China year three of (AD Zhejiang 1914). is For 50 a m. small The map schema adopts the original topographic map of the Republic of China year three (AD 1914). For a small portion of the missing area, topographic maps collected by the former National Government’s Sustainability 2020, 12, 537 5 of 18

Sustainability 2020, 12, 537 5 of 20 portion of the missing area, topographic maps collected by the former National Government’s Ministry ofMinistry the Interior of the organizedInterior organized by the Institute by the Institute of the Modern of the Modern History History Academia Academia Sinica Sinica were were used used as a supplementas a supplement [53]. [53]. AnotherAnother sourcesource ofof militarymilitary topographictopographic mapsmaps usedused inin thisthis studystudy waswas aa 1950s1950s aerialaerial surveysurvey mapmap fromfrom thethe U.S.U.S. ArmyArmy withwith aa scalescale ofof 1:250,000.1:250,000. During the Anti-JapaneseAnti‐Japanese War, thethe Sino-USSino‐US CooperativeCooperative AerialAerial SurveySurvey Team Team[54 [54]] conducted conducted detailed detailed aerial aerial photogrammetry photogrammetry of eastern of eastern China. China. In the In early the 1950s, early the1950s, Army the MapArmy Service Map Service of the USof the Corps US ofCorps Engineers of Engineers produced produced a set of a 1:250,000 set of 1:250,000 topographic topographic maps ofmaps eastern of eastern China China based onbased aerial on surveyaerial survey data [55 data]. These [55]. These are now are archived now archived in the Universityin the University of Texas of LibraryTexas Library Pavilion Pavilion [56]. This [56]. set This of maps set of has maps detailed has latitudedetailed and latitude longitude and longitude information information and projection and propertiesprojection properties that were usedthat were for spatial used for correction spatial correction and information and information extraction extraction during during the early the stage. early Comparedstage. Compared with this with set this of 1:50,000 set of 1:50,000 military military topographic topographic maps of maps the Republic of the Republic of China, of aerialChina, survey aerial mapssurvey have maps better have spatial better spatial coordinate coordinate information, information, so they so canthey use can the use same the same place place name name points points for spatialfor spatial referencing referencing and and improve improve accuracy. accuracy. Although Although the scalesthe scales of the of twothe two sets sets of topographic of topographic maps maps are diareﬀerent, different, we extract we extract all map all information map information in the ArcGIS in the 10.2ArcGIS platform 10.2 basedplatform on thebased results on ofthe geographic results of referencing,geographic referencing, and guarantee and the guarantee reliability the of reliability the results of through the results ﬁne-tuning through and fine compared‐tuning and with compared remote sensingwith remote imagery. sensing imagery.

2.3.2.3. Spatiotemporal Scope SinceSince this study only only involved involved the the urban urban areas areas and and towns towns in inthis this set setof topographic of topographic maps, maps, the theperiod period statistics statistics of the of themaps maps without without relevant relevant town town information information in the in map the map were were not counted. not counted. The Themapping mapping time time of the of the related related maps maps was was primarily primarily concentrated concentrated between between 1913 1913 and and 1936 1936 (Figure (Figure 44a),a), withwith aa medianmedian ofof 19171917 (Figure(Figure4 4b).b). Among Among them, them, the the highest highest frequency frequency occurred occurred in in 1916, 1916, accounting accounting forfor approximatelyapproximately 25%.25%. Therefore, it can be consideredconsidered thatthat thisthis setset ofof militarymilitary topographictopographic mapsmaps basicallybasically reﬂectsreflects thethe urbanurban andand towntown constructionconstruction informationinformation ofof ZhejiangZhejiang ProvinceProvince duringduring thethe earlyearly periodperiod ofof thethe RepublicRepublic ofof China.China. TheThe scope of this study is Zhejiang ProvinceProvince duringduring thethe earlyearly periodperiod ofof thethe RepublicRepublic ofof China.China.

0.25 （a） 1935 （b） 0.2 1930 0.15 1925 0.1 frequency 1920 0.05 1915 0 1905 1910 1915 1920 1925 1930 1935 1940 map year

FigureFigure 4.4. Histogram (a)) and box plot ((b)) ofof thethe periodperiod ofof thisthis setset ofof militarymilitary topographictopographic maps.maps. 2.4. Process Flow and the GIS-ESDA Model 2.4. Process Flow and the GIS‐ESDA Model The research process of this study primarily included the following three steps (Figure5). First, The research process of this study primarily included the following three steps (Figure 5). First, based on the detailed spatial coordinate information of the 1950s aerial survey map from the U.S. Army, based on the detailed spatial coordinate information of the 1950s aerial survey map from the U.S. a preliminary georeferencing of the set of 1:50,000 military topographic maps from the 1910s was Army, a preliminary georeferencing of the set of 1:50,000 military topographic maps from the 1910s performed. Then the position of the preliminary located topographic map was ﬁne-tuned based on the was performed. Then the position of the preliminary located topographic map was fine‐tuned based remote sensing image to ensure the accuracy of the spatial information. Finally, based on the Zipf rule on the remote sensing image to ensure the accuracy of the spatial information. Finally, based on the and the GIS-ESDA model, a spatial analysis and area measurement of the urban information on the Zipf rule and the GIS‐ESDA model, a spatial analysis and area measurement of the urban information historical topographic map was completed. on the historical topographic map was completed. Sustainability 2020, 12, 537 6 of 18 Sustainability 2020, 12, 537 6 of 20

Research 1950s aerial survey map of the 1910s military frame topographic map U.S. Army

Preliminary georeferencing Spatial georeferencing

Remote sensing image

Secondary georeferencing

Fine-tuning Fine-tuning

Georeferencing 1910s military topographic map

Urban area Zipf distribution Kernel density ESDA Spatial analysis measured in GIS rule analysis

Reconstruction of Urban system structure Spatial pattern Results historical urbanization

FigureFigure 5. 5.Process Process ﬂow flow and the GIS GIS-ESDA‐ESDA model. model.

2.4.1.2.4.1. Preliminary Preliminary Georeferencing Georeferencing TheThe spatial spatial positioning positioning of of this this set set of of topographic topographic maps primarily primarily included included two two steps. steps. First, First, this this set ofset topographic of topographic maps maps were were initially initially registered registered and positioned and positioned in the in ArcGIS the ArcGIS 10.2 platform 10.2 platform according to theaccording stitching to relationship. the stitching Second, relationship. based Second, on the spacebased coordinateon the space information coordinate ofinformation the aerial mapof the from the USaerial military, map from the the military US military, map of the the military 1910s wasmap spatiallyof the 1910s located was spatially according located to some according same to place namessome and same mountains place names or rivers. and mountains or rivers.

2.4.2.2.4.2. Fine-Tuning Fine‐Tuning of Topographicof Topographic Map Map Fine‐tuning of topographic map primarily included two steps. First, modern Landsat satellite Fine-tuning of topographic map primarily included two steps. First, modern Landsat satellite TM/ETM+ remote sensing images [57] were used to repeatedly fine‐tune the position of the TM/ETM+ remote sensing images [57] were used to repeatedly ﬁne-tune the position of the topographic topographic map based on obvious natural landmarks (such as mountains and rivers). Second, mapcontrol based points on obvious were utilized natural for landmarks overall error (such control as mountains and evaluation. and rivers). Second, control points were utilized for overall error control and evaluation. 2.4.3. Spatial Analysis 2.4.3. Spatial Analysis Zipf Distribution Rule Zipf Distribution Rule It is generally believed that the size distribution of cities in the different levels in a region follows aIt certain is generally law. G. believed K. Zipf proposed that the sizethe law distribution in 1949: of cities in the diﬀerent levels in a region follows a certain law. G. K. Zipf proposed the law in 1949: 𝑆 𝑆/𝑅 (1)

where Si is the scale of the i‐th city; S0 is the scaleSi = ofS0 the/R ifirst city; and Ri is the rank of the i‐th city. In (1) view of the fact that the Zipf distribution law is an ideal state of order‐scale distribution in cities, it is wheregenerallySi is the expressed scale of in the termsi-th of city; the SRotka0 is the model scale [58,59]: of the ﬁrst city; and Ri is the rank of the i-th city. In view of the fact that the Zipf distribution law is an ideal state of order-scale distribution in cities, 𝑆 𝑆 𝑅 (2) it is generally expressed in terms of the Rotka model [58,59]:

q S = S R− (2) i 0 × i Sustainability 2020, 12, 537 7 of 18 where q is the fractal dimension of the urban system. Generally, the logarithm of both sides of Equation (2) can be taken to obtain: ln S = ln S q ln R (3) i 0 − i Then the fractal dimension, q, is obtained using the method of least squares. Studies [60] have shown that when q > 1, the distribution of urban systems in the region is more concentrated, and large cities are prominent. When q < 1, the distribution of urban systems in the region is relatively scattered, and small or medium cities develop.

Exploratory Spatial Data Analysis, the ESDA The ESDA is a collection of spatial data analysis technologies. It primarily reveals spatial correlation characteristics and patterns by studying the spatial heterogeneity of data and the visualization of spatial distribution patterns [61]. The ESDA mainly includes a global spatial autocorrelation, a local spatial autocorrelation, a hotspot analysis, and a spatial trend analysis. This study used the Moran’s I index for the global spatial autocorrelation analysis [62,63]. The larger the Moran’s I index, the higher the degree of spatial autocorrelation in the region. The calculation method is: Pn Pn n i=1 j=1 wij(yi y) yj y I = − − (4) P P P 2 ( n n w ) n (y y) i=1 j=1 ij i=1 i − where n is the number of sample points; yi and yj are the attribute values of the sample points; and wij is the spatial weight matrix. * * The local spatial autocorrelation was identiﬁed using the Getis-Ord Gi index [62,63]. The Gi index is normalized, and the Z value is obtained. A higher Z value indicates that this area is a hot spot gathering area, while a lower Z value indicates that this area is a cold spot gathering area. * The calculation method for the Z value of the Getis-Ord Gi index is: P P n w x x n w j=1 ij j − j=1 ij Z Gi∗ = r r (5) P P P 2 n x2 [n n w2 ( n w ) ] j=1 j 2 j=1 ij− j=1 ij n (x) n 1 − − where n is the number of sample points; xi and yj are the attribute values of the sample points; and wij is the spatial weight matrix.

Kernel Density Analysis Kernel density analysis is a non-parametric test method that can be used to analyze the density of spatial point distributions. The basic principle is to estimate the theoretical distribution of sample points in a region using a kernel density function and to convert the density of discrete sample points into density values that are continuously distributed in space. A kernel density analysis can identify the concentrated area of a spatial point feature distribution; that is, the hotspot distribution area. The calculation method is: n 1 X x x F (x) = k( − i ) (6) n nr r i=1 where k ( ) is the kernel function; r is the analysis radius; and x x is the distance between the point x · − i to be estimated and the sample point xi.

GeoDa and ArcGIS Platforms The calculation process involved in this study was mainly performed using GeoDa 0.9.5 and ArcGIS 10.2 software platforms. The main calculation indicators include the Moran’s I Index, * the Getis-Ord Gi index, and the kernel density value. Sustainability 2020, 12, 537 8 of 20

GeoDa and ArcGIS Platforms The calculation process involved in this study was mainly performed using GeoDa 0.9.5 and

SustainabilityArcGIS 10.22020 software, 12, 537 platforms. The main calculation indicators include the Moran’s I Index,8 of the 18 Getis‐Ord Gi* index, and the kernel density value.

3.3. Results Results

3.1.3.1. Reconstruction Reconstruction of of Urban Urban Built Built up up Areas Areas BasedBased on on the the measured measured topographic topographic maps maps of of the the urban urban areas, areas, the the spatial spatial distribution distribution (Figure (Figure6 a)6a) andand built-up built‐up area area (Figure (Figure6 b)6b) of of each each city city or or town town were were obtained obtained using using the the ArcGIS ArcGIS 10.2 10.2 platform. platform. StatisticsStatistics show show that that during during the the period period of theof the Republic Republic of China of China (Table (Table1), there 1), werethere awere total a of total 252 citiesof 252 andcities towns and towns of various of various types types in the in Zhejiang the Zhejiang Province, Province, including including 75 75 cities cities at at the the county county level level and and above,above, 21 21 acropolises, acropolises, and and 156 156 towns. towns. The The total total built-up built‐up area area of of towns towns and and cities cities in in the the province province was was 2 2 140.590140.590 km km,2, of of which which the the total total built-up built‐up area area of of cities cities at at the the county county level level and and above above was was 91.764 91.764 km km2,, 2 2 withwith an an average average value value of of 1.223 1.223 km km.2. TheThe totaltotal built-up built‐up area area of of acropolises acropolises was was 8.051 8.051 km km,2, with with an an 2 2 averageaverage value value of of 0.383 0.383 km km.2 The. The total total built-up built‐up area area of of towns towns was was 40.775 40.775 km km, with2, with an averagean average value value of 2 0.261of 0.261 km .km Of2. all Of of all the of cities the andcities towns and intowns Zhejiang in Zhejiang Province, Province, the largest the built-up largest area built was‐up Hangxian, area was 2 whichHangxian, is the which capital is city the of capital the province, city of the with province, an area of with 14.778 an kmarea. of The 14.778 smallest km2 was. The Maqiao smallest town, was 2 HainingMaqiao County,town, Haining with an County, area of onlywith 0.023an area km of. only 0.023 km2.

FigureFigure 6. 6.Urban Urban spatial spatial distribution distribution ( a(a)) and and built-up built‐up area area ( b(b)) of of Zhejiang Zhejiang Province Province during during the the Republic Republic ofof China. China. Table 1. Statistics of cities and towns in the Zhejiang Province. Table 1. Statistics of cities and towns in the Zhejiang Province. Type No. Built-Up Area/km2 Mean Type No. Built‐Up Area/km2 Mean CountyCounty level and level above and above 75 75 91.764 91.764 1.223 1.223 Acropolis 21 8.051 0.383 TownAcropolis 15621 8.051 40.775 0.383 0.261 TotalTown 252156 40.775 140.590 0.261 0.557 Total 252 140.590 0.557 3.2. Spatial Pattern of Urbanization 3.2. Spatial Pattern of Urbanization The level of urbanization can be measured from the perspectives of urbanization of land and urbanizationThe level of population.of urbanization Because can urbanbe measured population from data the for perspectives historical periods of urbanization are diﬃcult of to land obtain, and thisurbanization study used of the population. ratio of the Because total area urban of urban population built-up areasdata andfor landhistorical area atperiods the county are difficult level scale to asobtain, urbanization this study rate. used the ratio of the total area of urban built‐up areas and land area at the county levelWith scale the as urbanization support of GeoDa rate. 0.9.5 and ArcGIS 10.2, the Moran’s I index Equation (7) of the county-level urbanization in Zhejiang Province during the 1910s was 0.296 (Z-Score = 4.992, p < 0.001). This indicated that its spatial distribution had signiﬁcant agglomeration characteristics (Figure7). More precisely, the high-value area of the urbanization level was adjacent to the high-value area, and the low-value area was adjacent to the low-value area. Sustainability 2020, 12, 537 9 of 20

With the support of GeoDa 0.9.5 and ArcGIS 10.2, the Moran’s I index Equation (7) of the county‐ level urbanization in Zhejiang Province during the 1910s was 0.296 (Z‐Score = 4.992, p < 0.001). This indicated that its spatial distribution had significant agglomeration characteristics (Figure 7). More precisely,Sustainability the2020 high, 12‐value, 537 area of the urbanization level was adjacent to the high‐value area, and the9 of 18 low‐value area was adjacent to the low‐value area.

Figure 7. Spatial autocorrelation characteristics of the urbanization level of Zhejiang Province in the Figure 7. Spatial autocorrelation characteristics of the urbanization level of Zhejiang Province in the Republic of China. Republic of China. As can be seen from Figure8a, among all county-level units, Hangxian County had the highest urbanizationAs can be seen rate from at 1.463%. Figure Jingning 8a, among County all county had the‐level lowest units, at 0.014%,Hangxian and County the overall had the urbanization highest urbanizationrate of Zhejiang rate at Province 1.463%. Jingning was 0.135%. County Further had analysisthe lowest of at the 0.014%, local spatial and the autocorrelation overall urbanization cold and rate of Zhejiang Province was 0.135%. Further analysis of the local spatial* autocorrelation cold and hot spots (Figure8b) showed that the Z-score of the Getis-Ord Gi index Equation (5) had obvious * hothigh–high spots (Figure/low–low 8b) showed aggregation that characteristics the Z‐score of in the the Getis spatial‐Ord distribution. Gi index Equation More precisely, (5) had the obvious hot spots high–high/low–lowwere primarily distributed aggregation in thecharacteristics Hang-Jia-Hu-Shao in the spatial plain area distribution. nearby Hangzhou More precisely, Bay, and the the hot cold spotsspot were concentration primarily distributed areas were nearin the west Hang Zhejiang‐Jia‐Hu‐ andShao southwest plain area Zhejiang. nearby Hangzhou The area Bay, near and Wenzhou the coldBay spot belonged concentration to the medium areas were distribution near west area Zhejiang of the Z-score and southwest value. The Zhejiang. spatial distribution The area near of the Wenzhoucold and Bay hot belonged spots were to the generally medium in distribution the direction area of northeast–southwest; of the Z‐score value. The that spatial is northeast distribution Zhejiang of the cold and hot spots were generally in the direction of northeast–southwest; that is northeast ProvinceSustainability belonged 2020, 12, 537 to hotspots and the southwest belonged to cold spots. 10 of 20 Zhejiang Province belonged to hotspots and the southwest belonged to cold spots.

Figure 8. Urbanization level at the county level (a) and the spatial hotspot distribution (b) of Zhejiang Figure 8. Urbanization level at the county level (a) and the spatial hotspot distribution (b) of Zhejiang Province in the Republic of China. Province in the Republic of China.

3.3. Urban System Structure The expressions of the rank‐scale distribution of all of the 252 cities and towns in Zhejiang Province during the Republic of China were obtained using the Zipf distribution rule (Equations (1)– (3)): y = −1.010x + 3.352 (r2 = 0.903, p < 0.001). According to Equation (3), we can see that the fractal dimension is q = 1.010 >1. It can be seen that, as a whole, the distribution of the Zhejiang urban system was relatively concentrated during the 1910s. Furthermore, the kernel density analysis method Equation (6) in ArcGIS 10.2 was used to obtain the spatial pattern of all cities and towns in Zhejiang Province, such as cities at the county level and above, towns, and acropolises. From Figure 9a, it can be seen that the kernel density values of spatial distribution of all of the 252 cities and towns in Zhejiang Province during 1910s formed two high‐value central areas on the north and south sides of Hangzhou Bay. In addition, two secondary nuclear density regions existed near the Ning‐shao Plain and Wenzhou Bay. From Figure 9b, it can be clearly seen that the spatial distribution of 75 county‐ level cities and above in Zhejiang Province is evenly distributed, and no relatively high‐value areas had been formed throughout the province. Since cities at the county level and above were basically political institutions in pre‐modern China, their even spatial distribution is conducive to administrative control. Sustainability 2020, 12, 537 10 of 18

3.3. Urban System Structure The expressions of the rank-scale distribution of all of the 252 cities and towns in Zhejiang Province during the Republic of China were obtained using the Zipf distribution rule (Equations (1)–(3)): y = 1.010x + 3.352 (r2 = 0.903, p < 0.001). According to Equation (3), we can see that the fractal − dimension is q = 1.010 >1. It can be seen that, as a whole, the distribution of the Zhejiang urban system was relatively concentrated during the 1910s. Furthermore, the kernel density analysis method Equation (6) in ArcGIS 10.2 was used to obtain the spatial pattern of all cities and towns in Zhejiang Province, such as cities at the county level and above, towns, and acropolises. From Figure9a, it can be seen that the kernel density values of spatial distribution of all of the 252 cities and towns in Zhejiang Province during 1910s formed two high-value central areas on the north and south sides of Hangzhou Bay. In addition, two secondary nuclear density regions existed near the Ning-shao Plain and Wenzhou Bay. From Figure9b, it can be clearly seen that the spatial distribution of 75 county-level cities and above in Zhejiang Province is evenly distributed, and no relatively high-value areas had been formed throughout the province. Since cities at the county level and above were basically political institutions in pre-modernSustainability 2020 China,, 12, 537 their even spatial distribution is conducive to administrative control.11 of 20

FigureFigure 9. Spatial 9. Spatial distribution distribution of the of kernelthe kernel density density map map of allof all the the cities cities (a )( county-levela) county‐level and and above above cities; (b) towns;cities; ((cb)) andtowns; acropolises (c) and acropolises (d). (d).

Nevertheless,Nevertheless, the the spatial spatial density density of of the the county-level county‐level cities cities and and above above in innorthern northern Zhejiang Zhejiang was was still higherstill higher than than that that in southern in southern Zhejiang, Zhejiang, which which isis consistent with with the the fact fact that that the the northern northern counties counties in Zhejiangin Zhejiang are more are more distributed distributed than than the the southern southern ones.ones. According According to to the the spatial spatial distribution distribution kernel kernel density maps of 156 towns in the province (Figure 9c), it can be seen that the spatial distribution density maps of 156 towns in the province (Figure9c), it can be seen that the spatial distribution pattern pattern is basically consistent with the spatial structure of all cities and towns in Zhejiang Province is basically(Figure consistent 9a). Moreover, with a the high spatial value structurerange of kernel of all density cities and is primarily towns in distributed Zhejiang on Province the both (Figure sides 9a). of Hangzhou Bay and areas such as the Ning‐Shao Plain. This also shows that the urban system pattern of Zhejiang Province during the Republic of China was primarily determined by the spatial distribution of towns. The areas, such as the Hang‐Jia‐Hu Plain and the Ning‐Shao Plain, have always developed market economies, so a large number of towns have developed under county‐level cities. Due to historical reasons, such as resistance to pirating and coastal defenses, Zhejiang’s coastal areas also have had a large number of acropolises. From Figure 9d, it can be seen that the areas with high kernel density values in the urban spatial distribution of the acropolis are primarily concentrated along the coast of eastern Zhejiang and the southern shore of Hangzhou Bay. In addition, another high kernel density area formed in the Wenzhou Bay area.

3.4. Trend Analysis From an analysis of the spatial trend of the built‐up areas of all of the 252 cities and towns (Figure 10a), it can be seen that the distribution trend rises from west to east and rises from south to north. Sustainability 2020, 12, 537 11 of 18

Moreover, a high value range of kernel density is primarily distributed on the both sides of Hangzhou Bay and areas such as the Ning-Shao Plain. This also shows that the urban system pattern of Zhejiang Province during the Republic of China was primarily determined by the spatial distribution of towns. The areas, such as the Hang-Jia-Hu Plain and the Ning-Shao Plain, have always developed market economies, so a large number of towns have developed under county-level cities. Due to historical reasons, such as resistance to pirating and coastal defenses, Zhejiang’s coastal areas also have had a large number of acropolises. From Figure9d, it can be seen that the areas with high kernel density values in the urban spatial distribution of the acropolis are primarily concentrated along the coast of eastern Zhejiang and the southern shore of Hangzhou Bay. In addition, another high kernel density area formed in the Wenzhou Bay area.

3.4. Trend Analysis From an analysis of the spatial trend of the built-up areas of all of the 252 cities and towns

(FigureSustainability 10 a),2020 it, 12 can, 537 be seen that the distribution trend rises from west to east and rises from12 south of 20 to north. This also shows that the spatial distribution of all the urban areas in Zhejiang Province Thishas a also trend shows of being that the high spatial in the distribution northeast and of all low the in urban the southwest. areas in Zhejiang The spatial Province trend has analysis a trend of beingthe urbanization high in the levelnortheast of the and 75 low county-level in the southwest. regions inThe Zhejiang spatial Provincetrend analysis also indicatesof the urbanization this result level(Figure of 10theb). 75 county‐level regions in Zhejiang Province also indicates this result (Figure 10b).

(a) Z

(b) Z

Figure 10. TrendTrend analysis of the built built-up‐up areas of cities and towns ( a) and the urbanization level in Zhejiang Province (b). (x-axis arrow direction indicates east; y-axis arrow direction indicates north; Zhejiang Province (b). (x‐axis arrow direction indicates east; y‐axis arrow direction indicates north; z‐ z-axis arrow direction indicates built-up areas or the urbanization level.). axis arrow direction indicates built‐up areas or the urbanization level.).

4. Discussion

4.1. Uncertainty Analysis Although the military topographic map of the Republic of China used in the reconstruction of urban built‐up areas in this study was completed using modern surveying and mapping technology, the technological conditions and cartographic levels at that time had historical limitations. Therefore, it is necessary to evaluate the errors of these topographic maps and perform an uncertainty analysis. By referring to the principle of the “point‐to‐point” test [26,42], 26 relevant control points were randomly selected in the military topographic map after secondary correction and fine‐tuning according to the principle of uniform distribution throughout the province. These were then compared with the corresponding position in the TM/ETM+ remote sensing image. The error range between the control points on the remote sensing image and the points on the topographic map were calculated in ArcGIS 10.2 (Table 2), and error range distribution map was interpolated by kriging method in ArcGIS 10.2 (Figure 11). The error range of the 26 control points was between 0.142 and 1.348 km, and the average error was 0.553 km. Considering that these sets of military topographic Sustainability 2020, 12, 537 12 of 18

4. Discussion

4.1. Uncertainty Analysis Although the military topographic map of the Republic of China used in the reconstruction of urban built-up areas in this study was completed using modern surveying and mapping technology, the technological conditions and cartographic levels at that time had historical limitations. Therefore, it is necessary to evaluate the errors of these topographic maps and perform an uncertainty analysis. By referring to the principle of the “point-to-point” test [26,42], 26 relevant control points were randomly selected in the military topographic map after secondary correction and ﬁne-tuning according to the principle of uniform distribution throughout the province. These were then compared with the corresponding position in the TM/ETM+ remote sensing image. The error range between the control points on the remote sensing image and the points on the topographic map were calculated in ArcGIS 10.2 (Table2), and error range distribution map was interpolated by kriging method in ArcGIS 10.2 (Figure 11). The error range of the 26 control points was between 0.142 and 1.348 km, and the average error was 0.553 km. Considering that these sets of military topographic maps had relatively small errors after secondary correction and projection ﬁne-tuning, they can be used for the reconstruction of relevant geographical elements in this area.

Table 2. Military topographic maps error range of Zhejiang Province in the Republic of China.

Control Point on Topographic Maps Point on Remote Sensing Image Error No. Range/km Point Longitude/◦ E Latitude/◦ N Longitude/◦ E Latitude/◦ N 1 Budaiao 120.518 27.153 120.520 27.152 0.248 2 Taishun 119.708 27.560 119.710 27.557 0.346 3 Pingyang 120.562 27.663 120.565 27.662 0.337 4 Ruian 120.643 27.792 120.647 27.794 0.486 5 Shiguocun 120.286 28.125 120.289 28.129 0.520 6 Longquan 119.109 28.073 119.113 28.073 0.446 7 Suichang 119.272 28.597 119.274 28.599 0.306 8 Lishui 119.934 28.438 119.938 28.438 0.444 9 Huangyan 121.253 28.653 121.258 28.654 0.568 10 Bapaimen 121.891 29.070 121.897 29.074 0.812 11 Tianwangshan 122.089 29.755 122.096 29.753 0.811 12 Yuyao 121.151 30.045 121.153 30.049 0.418 13 Linshan 120.991 30.150 120.992 30.151 0.142 14 Puyangjiang 120.17 30.105 120.176 30.106 0.686 15 Sandun 120.077 30.322 120.083 30.324 0.702 16 Haining 120.541 30.408 120.544 30.410 0.372 17 Xincang 121.177 30.730 121.187 30.723 1.348 18 Jiaxing 120.741 30.753 120.745 30.755 0.517 19 Xiaofeng 119.548 30.587 119.553 30.587 0.555 20 Jiakou 119.035 30.131 119.039 30.125 0.782 21 Longyou 119.182 29.050 119.187 29.052 0.597 22 Lanxi 119.466 29.202 119.469 29.203 0.377 23 Weishan 120.444 29.318 120.444 29.323 0.566 24 Xinchang 120.908 29.501 120.909 29.505 0.522 25 Tonglu 119.676 29.810 119.68 29.811 0.448 26 Xiaoershan 118.118 29.271 118.127 29.269 1.025 Sustainability 2020, 12, 537 13 of 20

maps had relatively small errors after secondary correction and projection fine‐tuning, they can be used for the reconstruction of relevant geographical elements in this area.

Table 2. Military topographic maps error range of Zhejiang Province in the Republic of China.

Point on Topographic Maps Point on Remote Sensing Image Error No. Control Point Longitude/° E Latitude/° N Longitude/° E Latitude/° N Range/km 1 Budaiao 120.518 27.153 120.520 27.152 0.248 2 Taishun 119.708 27.560 119.710 27.557 0.346 3 Pingyang 120.562 27.663 120.565 27.662 0.337 4 Ruian 120.643 27.792 120.647 27.794 0.486 5 Shiguocun 120.286 28.125 120.289 28.129 0.520 6 Longquan 119.109 28.073 119.113 28.073 0.446 7 Suichang 119.272 28.597 119.274 28.599 0.306 8 Lishui 119.934 28.438 119.938 28.438 0.444 9 Huangyan 121.253 28.653 121.258 28.654 0.568 10 Bapaimen 121.891 29.070 121.897 29.074 0.812 11 Tianwangshan 122.089 29.755 122.096 29.753 0.811 12 Yuyao 121.151 30.045 121.153 30.049 0.418 13 Linshan 120.991 30.150 120.992 30.151 0.142 14 Puyangjiang 120.17 30.105 120.176 30.106 0.686 15 Sandun 120.077 30.322 120.083 30.324 0.702 16 Haining 120.541 30.408 120.544 30.410 0.372 17 Xincang 121.177 30.730 121.187 30.723 1.348 18 Jiaxing 120.741 30.753 120.745 30.755 0.517 19 Xiaofeng 119.548 30.587 119.553 30.587 0.555 20 Jiakou 119.035 30.131 119.039 30.125 0.782 21 Longyou 119.182 29.050 119.187 29.052 0.597 22 Lanxi 119.466 29.202 119.469 29.203 0.377 23 Weishan 120.444 29.318 120.444 29.323 0.566 24 Xinchang 120.908 29.501 120.909 29.505 0.522 Sustainability25 2020Tonglu, 12, 537 119.676 29.810 119.68 29.811 0.44813 of 18 26 Xiaoershan 118.118 29.271 118.127 29.269 1.025

Figure 11. Map of error range distribution in Zhejiang Province. Figure 11. Map of error range distribution in Zhejiang Province. 4.2. Comparison of the Perimeter of the City Wall and Historical Records 4.2. Comparison of the Perimeter of the City Wall and Historical Records During the historical period, various national archives and chronicles have generally been recorded in the shapeDuring of the cityhistorical and the period, perimeter various of the citynational wall, butarchives they are and often chronicles described have non-quantitatively generally been inrecorded the form in of the text shape or hand-drawn of the city maps.and the For perimeter example, of during the city the wall, Qing but Dynasty they are (AD often 1644–1911) described in non the ‐ Emperor Jiaqing Restoration Unification Book, the city wall form of Fuyang County in Zhejiang Province was recorded as “The perimeter is six li. Four gate. There is a ring moat around the city. Southeast of the city near the river ... ” He et al. [64] made full use of the city wall perimeter data recorded in the historical books and obtained the city area by considering the shape of the city as a square or a circle. They also obtained a set of built-up areas of cities in the 18 provinces of the Qing Dynasty. Since the construction of the city wall was basically not done in Zhejiang Province after the period of the Republic of China, the city information reflected on the topographic map of the Republic of China can be considered to represent the perimeter. Therefore, it can be considered the actual city wall shape and area of the city during the Qing Dynasty. Based on the city wall perimeter data recorded in the local chronicles and in the Emperor Jiaqing Restoration Unification Book, the city wall perimeter information of 55 cities above the county level was obtained, including 12 prefecture-level cities. In addition, the unit of measurement of the length of the Qing Dynasty li was converted according to 1 li = 0.576 km [65], and the area formula is:

2 Ai = (Ci/4) (7) where Ai is the area of the i-th city; and Ci is the circumference of the i-th city. The city wall perimeter was converted into the area (km2). By comparing the historical city wall perimeter data and city wall perimeter data based on the topographic map of the Republic of China (Figure 12a), it was found that the correlation coefficient between the two groups reached 0.908 (p < 0.001). It can be seen that, although the city perimeter data recorded in the historical books are limited in terms of the approximate number and low precision, the overall length is significantly consistent with the actual city perimeter length reconstructed from the military topographic map. The results of the regression analysis showed that the city perimeter data recorded in the historical books was approximately 20% higher than the measured value. It should be noted that, since Hangxian is the capital of Zhejiang Province, it has a large wall perimeter and area than other cities. After excluding Hangxian data from the model, the correlation coefficient is 0.834 (p < 0.001). Sustainability 2020, 12, 537 14 of 20

quantitatively in the form of text or hand‐drawn maps. For example, during the Qing Dynasty (AD 1644–1911) in the Emperor Jiaqing Restoration Unification Book, the city wall form of Fuyang County in Zhejiang Province was recorded as “The perimeter is six li. Four gate. There is a ring moat around the city. Southeast of the city near the river …” He et al. [64] made full use of the city wall perimeter data recorded in the historical books and obtained the city area by considering the shape of the city as a square or a circle. They also obtained a set of built‐up areas of cities in the 18 provinces of the Qing Dynasty. Since the construction of the city wall was basically not done in Zhejiang Province after the period of the Republic of China, the city information reflected on the topographic map of the Republic of China can be considered to represent the perimeter. Therefore, it can be considered the actual city wall shape and area of the city during the Qing Dynasty. Based on the city wall perimeter data recorded in the local chronicles and in the Emperor Jiaqing Restoration Unification Book, the city wall perimeter information of 55 cities above the county level was obtained, including 12 prefecture‐level cities. In addition, the unit of measurement of the length of the Qing Dynasty li was converted according to 1 li = 0.576 km [65], and the area formula is:

𝐴 𝐶/4 (7)

where Ai is the area of the i‐th city; and Ci is the circumference of the i‐th city. The city wall perimeter was converted into the area (km2). By comparing the historical city wall perimeter data and city wall perimeter data based on the topographic map of the Republic of China (Figure 12a), it was found that the correlation coefficient between the two groups reached 0.908 (p < 0.001). It can be seen that, although the city perimeter data recorded in the historical books are limited in terms of the approximate number and low precision, the overall length is significantly consistent with the actual city perimeter length reconstructed from the military topographic map. The results of the regression analysis showed that the city perimeter data recorded in the historical books was approximately 20% higher than the measured value. It should be noted that, since Hangxian is the capital of Zhejiang SustainabilityProvince,2020 it, 12 has, 537 a large wall perimeter and area than other cities. After excluding Hangxian data from14 of 18 the model, the correlation coefficient is 0.834 (p < 0.001).

25 30 （a）（b） y=1.262x-0.456 25 y=1.824x-0.384

20 2 r2=0.823 r2=0.911 20 15 15 10 10 historical data/km

5 calculated data/km 5

0 0 0 5 10 15 0 5 10 15 measured data/km 2 measured data/km FigureFigure 12. Correlation12. Correlation of of the the city city wall wall perimeter perimeter (a) or urban urban area area (b ()b between) between historical historical data data and and measuredmeasured data. data.

Similarly,Similarly, there there is a is significant a significant correlation correlation betweenbetween the the measured measured city city area area and and the thecity cityarea area calculatedcalculated using using Equation Equation (7) (7) (Figure (Figure 12 12b),b), and and the the correlationcorrelation coefficient coefficient is is 0.955 0.955 (p < (p 0.001).< 0.001). The area The area calculatedcalculated using using Equation Equation (7) (7) is approximatelyis approximately 80%80% higher than than the the measured measured area. area. The The correlation correlation coefficoefficientcient is 0.843 is 0.843 (p < (p0.001) < 0.001) after after excluding excluding Hangxian Hangxian data data from from the the model. model.

Sustainability 2020, 12, 537 15 of 20 4.3. Comparison of Land Urbanization and Population Urbanization Land4.3. Comparison urbanization of Land and Urbanization population and Population urbanization Urbanization are two aspects of the urbanization process. As detailedLand statistics urbanization of urban and population land and population urbanization in are history two aspects are often of the subject urbanization to many process. limitations, As the relationshipdetailed statistics between of urban the two land groups and population needs to bein history further are examined. often subject Fortunately, to many limitations, the Ministry the of the Interiorrelationship of the National between Government the two groups during needs the to periodbe further of the examined. Republic Fortunately, of China conductedthe Ministry preliminary of the statisticsInterior on theof populationthe National of Government counties in Zhejiangduring the Province. period Theof the report Republic “Statistics of China of Provinces, conducted Cities, preliminary statistics on the population of counties in Zhejiang Province. The report “Statistics of and Villages of Zhejiang Province” in the fourth Internal Affairs Survey Statistics Form in December 1933 Provinces, Cities, and Villages of Zhejiang Province” in the fourth Internal Affairs Survey Statistics published in detail the total population of counties and towns, with the exception of the provincial Form in December 1933 published in detail the total population of counties and towns, with the capitalexception population. of the The provincial records capital reported population. data from The January records 1931 reported to May data 1933. from ThisJanuary batch 1931 of to data May can be considered1933. This to reflect batch of the data population can be considered distribution to reflect of Zhejiang the population Province distribution in the early of Zhejiang and middle Province period of thein Republic the early and of China. middle period In addition, of the Republic the population of China. data In addition, of Hangxian, the population the provincial data of Hangxian, capital, was supplementedthe provincial by thecapital,Monthly was supplemented Statistics (No. by 26) the of Monthly the Republic Statistics of China (No. 26)in 1934.of the Republic of China in A1934. regression analysis of the urban area of the county and the total population of the county in ZhejiangA regression Province analysis for the of middle the urban period area of the the county Republic and ofthe China total population showed thatof the the county correlation in coeffiZhejiangcient between Province them for was the 0.792middle (p period< 0.001) of (Figure the Republic 13a). Itof can China be seenshowed that that there the was correlation a significant coefficient between them was 0.792 (p < 0.001) (Figure 13a). It can be seen that there was a significant correlation between the urban area of the county and the total population. Generally, the area of correlation between the urban area of the county and the total population. Generally, the area of the the county with a large population has a larger urban built-up area. After excluding Hangxian data county with a large population has a larger urban built‐up area. After excluding Hangxian data from fromthe the model, model, the the correlation correlation coefficient coefficient is is0.810 0.810 (p (

150 80

y=6.105x+15.949 （a）（b） 2 y=32.095x+3.878 r =0.626 60 2 100 r =0.639

50 20 total population/10000

0 population urbanization rate/% 0 0 5 10 15 20 0 0.5 1 1.5 2 land urbanization rate/% urban area/km FigureFigure 13. 13.(a) (a The) The relationship relationship between between the the urban urban area and area the and total the population total population of the county; of the(b) The county; (b) Therelationship relationship between between land landurbanization urbanization and population and population urbanization. urbanization.

Although there was no direct urban demographic data in the Internal Affairs Survey Statistics Form, the county town population and general town population can be used to approximate the urban population in a certain county. In addition, the population urbanization level of the county can be obtained using this method. The calculation results showed that the overall population urbanization rate in Zhejiang Province was 13.49%, with the highest value of Hangxian being 64.71% and the lowest value of Qingyuan being 1.08%. The regression analysis of the land urbanization level and the population urbanization level showed that the correlation coefficient between them was 0.799 (p < 0.001) (Figure 13b). It should be noted that the reason for the high urbanization rate of the population in Hangxian County might be because the original data included the entire population of the provincial capital, but it did not distinguish between urban and agricultural populations. After excluding Hangxian data from the model, the correlation coefficient is 0.609 (p < 0.001). Despite this fact, the coupling relationship between land urbanization and population urbanization can still be studied using the two sets of data.

Sustainability 2020, 12, 537 15 of 18

Although there was no direct urban demographic data in the Internal Affairs Survey Statistics Form, the county town population and general town population can be used to approximate the urban population in a certain county. In addition, the population urbanization level of the county can be obtained using this method. The calculation results showed that the overall population urbanization rate in Zhejiang Province was 13.49%, with the highest value of Hangxian being 64.71% and the lowest value of Qingyuan being 1.08%. The regression analysis of the land urbanization level and the population urbanization level showed that the correlation coefficient between them was 0.799 (p < 0.001) (Figure 13b). It should be noted that the reason for the high urbanization rate of the population in Hangxian County might be because the original data included the entire population of the provincial capital, but it did not distinguish between urban and agricultural populations. After excluding Hangxian data from the model, the correlation coefficient is 0.609 (p < 0.001). Despite this fact, the coupling relationship between land urbanization and population urbanization can still be studied using the two sets of data.

5. Conclusions and Prospects (1) During the period of the Republic of China, there were a total of 252 cities and towns in Zhejiang Province including 75 cities at or above the county level, 21 cities in acropolis, and 156 towns. The total built-up area of towns and cities in the province was 140.590 km2, of which the total built-up area of cities at the county level and above was 91.764 km2, with an average value of 1.223 km2. The total built-up area of the acropolis was 8.051 km2, with an average value of 0.383 km2. In addition, the total built-up area of towns was 40.775 km2, with an average value of 0.261 km2. (2) During the period of the Republic of China, the county-level urbanization level of Zhejiang Province had significant agglomeration characteristics. Among the county-level units, the urbanization rate was highest in Hangxian County at 1.463%, and lowest in Jingning County at 0.014%. The province’s overall land urbanization rate was 0.135%. An analysis of the cold and hot spots showed that the hot spots were primarily distributed in the Hang-Jia-Hu-Shao area near Hangzhou Bay. The cold spots were primarily concentrated in the western and southwestern Zhejiang. The region near Wenzhou Bay belonged to the medium distribution area of the Z value. (3) According to the Zipf distribution rule, it was concluded that the distribution of Zhejiang’s urban system during the Republic of China belonged to the centralized distribution type. The results of the kernel density analysis showed that the urban spatial distribution formed two high-value central areas on the north and south sides of Hangzhou Bay and two secondary high-density areas near the Ning-Shao Plain and Wenzhou Bay. (4) The correlation coefficient between the city wall perimeter data recorded in the local chronicles and the measured city wall perimeter was 0.908, showing that the historical books and chronicles had high reliability. However, the results of the regression analysis showed that the city perimeter data recorded in the historical books was approximately 20% higher than the measured value. (5) The province’s overall urbanization rate was 13.49%, of which the highest value of Hangxian County was 64.71% and the lowest value of Qingyuan County was 1.08%. During the period of the Republic of China, there was a significant correlation between the urbanization rate of county land and the urbanization rate of the population, and the correlation coefficient was 0.799. This study systematically demonstrated the relationship between the city wall perimeter data in historical local chronicles and the measured urban built-up area using the 1:50,000 large-scale topographic map data during the Republic of China, which was in pre-modern China. Additionally, according to the regression analysis, the relational equation of the historical city wall perimeter and the measured urban area was obtained. Most parts of China have relatively systematic historical records of the city wall perimeter that can be converted to the urban area using the relationship formula, thus providing basic data for a long-term urbanization level sequence study. In addition, by performing a comparison with the population data of Zhejiang Province during the Republic of China, the correlation between the land urbanization and the population urbanization data was obtained. This correlation Sustainability 2020, 12, 537 16 of 18 provides an empirical research result for understanding the relationship between different measures of urbanization during historical periods. However, due to the accuracy limitations of historical data, the urban built-up area reconstructed in this paper can only represent the situation in the early and middle period of the Republic of China. The coupling relationship between land urbanization and population urbanization is only a preliminary discussion. Later land-use reconstruction work requires in-depth research on the spatial projection correction and the control of reconstruction errors to ensure the reliability of reconstruction data. Additionally, the spatial and temporal resolution of land use reconstruction for an historical period should be further improved, and the dynamic pattern evolution analysis of land use and urbanization should be performed on a more detailed scale.

Author Contributions: Z.W. and F.L. conceived and designed the experiments; Z.W., X.C., M.J. (Min Ju), C.L. and M.J. (Meixin Jiang) performed the model; Z.W., G.L. and Y.J. analyzed the data; Z.W. wrote the paper. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by National Natural Science Foundation of China (41761045 and 41561020) and Open fund of Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection (STKF201909). Acknowledgments: We thank anonymous referees and the editor for their helpful comments on this paper. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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