Journal of Global Change Data & Discovery2020.4(1):69-75

[PDF] [DATASET]

Citation:Chen, Y. R., Tu, T. Q., Song, W. XMiddle and Primary Public School Districts Dataset in Nanjing Urban Area (2008, 2018)[J]. Journal of Global Change Data & Discovery,2020.4(1):69-75 .DOI: 10.3974/geodp.2020.01.10 .

DOI: 10

Middle and Primary Public School Districts Dataset of in Nanjing Urban Area (2008, 2018)

Chen, Y. R.1,2  Tu, T. Q. 2,3  Song, W. X. 1,4*

1. Nanjing Institute of Geography and Limnology, Nanjing 210008, Jiangsu, China;

2. University of Chinese Academy of Sciences, Beijing 100049, China;

3. Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China;

4. Nanjing Institute of Geography and Limnology, Key Laboratory of Watershed Geographic Sciences, Chinese Academy of Sciences, Nanjing 210008, Jiangsu, China

 

Abstract: Nanjing’s main urban area consists of an inner urban area enclosed by the Ming Dynasty City Wall and the surrounding areas, namely Xuanwu District, Gulou District, Qinhuai District, Jianye District, Yuhuatai District (northeast part), Jiangning District (northern part), and Qixia District (west side). Relying primarily on text-based data, specifically descriptions from education resources of compulsory public school district in Nanjing’s main urban area, for 2008 and 2018, the author first confirmed the location of each school and the scope of its school district, and then sorted and compiled the data set according to year (2008, 2018), by referring to the Open Street Map and Baidu Map with the support of ArcGIS software. The final data set includes the following: (1) Raster data of public primary school locations (.tif); (2) School district distribution data of public primary schools, comprising their school district scope, school name, presence or absence of branch schools, the school area, and admission rate of Nanjing Foreign Language School (.shp); (3) Raster data of public junior high school locations (.tif); (4) School district distribution data of public junior high schools, comprising their school district scope, school name, school area, and average score data for the senior high school entrance examination (.shp). The full data set is stored in .TIF, .SHP formats and consists of 56 data files. The data volume is 8.08 MB (compressed to 245 KB). The analysis and research results based on this data set appeared in a Geography Research article (volume 38, issue 8) published in 2019.

Keywords: Compulsory education resources; junior high school and primary school; school district; spatial differences; Nanjing

1 Introduction 

The development of education is related to the comprehensive national strength and international competitiveness of the country. Compulsory education, which is universal, obligatory, and cost-free, forms the basis for improving national quality and achieving social equity[1]. The 19th National Congress of the Communist Party of China has committed to giving priority to a balanced system of education, as fairness in education has become an important issue in modern society[2]. However, under the constraints of the “school district system” policy, the unbalanced and unfair allocation of compulsory education resources has gradually become apparent, which presents a serious problem[3-4].

The spatial pattern of educational facilities and the fair allocation of educational resources have become significant issues for social geographers, both at home and abroad[5-7]. Currently, urban geographers mainly evaluate the balance and distribution of urban educational resources from the perspectives of space–time accessibility, spatial distribution of educational resources, and school clustering and spatial pattern evolution, often with the help of GIS network analysis, the shortest time method, trend surface analysis in addition to other technologies[8-12]. There are also scholars who build equilibrium constraint models from variables, such as house prices, population, transportation, terrain, and the distance between schools, to analyze the availability of educational resources[13-17]. The National Education Department has issued a series of policies to promote the balanced development of educational resources. Whether or not the allocation of educational resources directly affects the development of education[18], especially under the “school district system” policy—although the phenomenon of “school selection by score” and “school selection by money” has waned—with the sharp rise in the mean price of “school district housing”, the phenomenon of “school selection by housing” has greatly affected how fair the urban education resource allocation is in mainland China[19].

Optimizing the allocation of educational resources and realizing substantial fairness of this allocation are indispensable to, and play a significant role in, the development of education [20]. As an important base of scientific research and education in China, Nanjing has amassed a wealth of educational culture, which may better capture the spatial configuration of educational resources in China's mega–cities. This data set explores the spatial pattern and evolution of primary school and junior high school locations and their school district division in Nanjing, which could provide a new perspective for the study of social problems such as class differentiation and residential space differentiation, thereby making a contribution to advancing the fair allocation of compulsory education resources in China, and alleviating the education gap between different socioeconomic classes in urban internal space.

2 Metadata of Dataset

The metadata of the dataset[21] is summarized in Table 1. It includes dataset’s full name, short name, authors, geographical area, calendar years, temporal resolution, spatial resolution, data format, data size, data files, data publisher, and data sharing policy.

3 Data Source and Study Areas

3.1 Data Source

According to the education map and the list of schools on the website of the Education Bureau of Nanjing City and other administrative districts, data on the spatial distribution of the primary school and junior high school educational resources, their school area, school district, and other basic education facilities in the main urban area of Nanjing in 2018 were obtained. The corresponding data in 2008 come from the data contained in the “Planning of Primary School and Junior High School in Nanjing (20062020)”, “The Statistical Yearbook of Nanjing” and the 2008 edition of the map of Nanjing[23–25]. Data on Nanjing Foreign Language School’s admission rate and the average score of junior high school entrants were compiled from the Municipal Education Bureau as well as the Soxue.com, and the data of campus were compiled from Soxue.com[26–28]. For some schools that could not be found, their respective area was digitized by using Google map satellite imagery and calculated in ArcGIS software. The spatial database of educational facilities in Nanjing’s primary

Table 1  Metadata summary of the “School districts dataset of middle and primary public school in
Nanjing urban area (2008, 2018)”

Items

Description

Dataset full name

School districts dataset of middle and primary public school in Nanjing urban area (2008, 2018)

Dataset short name

Middle&PrimarySchoolDistrict_Nanjing

Authors

Chen, Y. R. AAA-9864-2019, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, chenyanru18@mails.ucas.ac.cn

Tu, T. Q. AAA-9931-2019, Institutes of Science and Development, Chinese Academy of Sciences, sgos1101@126.com

 

Song, W. X. N-1173-2018, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, wxsong@niglas.ac.cn

Geographical region

Nanjing main urban area (31°57'10"N-32°09'43"N, 118°39'52"E-118°54'10"E)

Year

2008 and 2018           Temporal resolution   Year

Spatial resolution

30 m ´ 30 m             Data format   .tif, .shp

Data size

8.08MB (before compression), 245KB (after compression)

Data files

1. Location raster data (.tif) of public primary schools; 2. School district distribution data (.shp) of public primary schools; 3. Location raster data (.tif) of public junior high schools; 4. School district distribution data of public junior high schools (.shp)

Foundation

National Natural Science Foundation of China (41771184)

Data publisher

Global Change Research Data Publishing & Repository, http://www.geodoi.ac.cn

Address

No. 11A, Datun Road, Chaoyang District, Beijing 100101, China

Data sharing policy

Data from the Global Change Research Data Publishing & Repository includes metadata, datasets (data products), and publications (in this case, in the Journal of Global Change Data & Discovery). Data sharing policy includes: (1) Data are openly available and can be free downloaded via the Internet; (2) End users are encouraged to use Data subject to citation; (3) Users, who are by definition also value-added service providers, are welcome to redistribute Data subject to written permission from the GCdataPR Editorial Office and the issuance of a Data redistribution license; and (4) If Data are used to compile new datasets, the ‘ten percent principal’ should be followed such that Data records utilized should not surpass 10% of the new dataset contents, while sources should be clearly noted in suitable places in the new dataset[22]

Communication and searchable system

DOI, DCI, CSCD, WDS/ISC, GEOSS, China GEOSS

 

schools and junior high schools was established using the Baidu Map tool, by collecting the longitude and latitude information of the schools’ geographical position, and then arranging, compiling, and analyzing this collected data. As a result of the 2013 administrative territorial entity adjustment in Nanjing, this data set has been revised for primary schools and junior high schools in 2008 based on the latest (2018) administrative territorial entity’s location.

3.2 Study Area

Nanjing is an important regional core city of the Yangtze River Delta integration, and also a typical representative of second-tier cities in China. With its long history, profound cultural accumulation, and significant modern urbanization features, Nanjing is apt to reflect the spatial configuration of educational resources in China’s major cities. Therefore, as our study area, Nanjing has the significance of representativeness, universality and diversity. Nanjing city is composed of 11 districts. The research scope of this data set covers the main urban area of Nanjing, consisting of Xuanwu District, Gulou District, Qinhuai District, Jianye District, Yuhuatai District (northeastern part), Jiangning District (northern part) and Qixia District (western part).

3.3 Technical Route of Data Development

Technical Route: Data on schools’ distribution in the Nanjing’s main urban area, in 2008 and 2018 respectively, were collected and collated. ArcGIS, with reference to, for example, Open Street Map and Baidu Map, was used to obtain each school’s location latitude and longitude, school area distribution, school location points for rasterizing, and school area distribution boundaries for vector processing. First, according to the number, area and grade of

 

Figure 1  Technical roadmap

the schools, we evaluated the spatial and temporal differences in the scale and quality of compulsory education resources; then, we visualized the spatial distribution of primary schools and junior high schools, in both 2008 and 2018, from which we analyzed the growth and change in the number structure of primary schools and junior high schools from 2008 to 2018; based on those results, we next summarize the spatial distribution differences; finally, we compared the evolution characteristics of primary school and junior high school districts from 2008 to 2018, and analyze the differences in the distribution.

4 Results and Validation

4.1 Data Products

The dataset has four parts (1) Raster data of public primary school locations (.tif); (2) School district distribution data of public primary schools, comprising their school district scope, school name, presence or absence of branch schools, the school area, and admission rate of Nanjing Foreign Language School (.shp); (3) Raster data of public junior high school locations (.tif); (4) School district distribution data of public junior high schools, comprising their school district scope, school name, school area, and average score data for the senior high school entrance examination (.shp).

4.2 Data Results

According to the data set compiled for Nanjing’s primary schools and junior high schools, from 2008 to 2018, the number of primary school districts in the study area decreased from 153 to 143, the total area of schools increased from 1.51 km2 to 1.61 km2, and the number of junior high schools increased from 47 to 50, while the total area of school increased from 1.28 km2 to 1.42 km2 (Table 2). Despite a reduction in the number of primary schools, due to the merger of inner-city schools and the splitting and expansion of peripheral schools, the total area of schools has increased, the scale of primary education facilities has expanded, and the number of junior high schools has increased by 3, but the total area of all schools has only increased by 0.14 km2, with little change evident in their scale.

Setting aside the difficulty of the examination paper, the schools with high score have strong stability and degree of educational quality aggregation is thus relatively balanced. Generally, the imbalance in the education quality of junior high schools is obviously less than that of primary schools[29].

It can be seen from Figure 2 that, for primary schools, the core–periphery spatial structure of their distribution in Nanjing is remarkable, showing a pattern expanding from the central urban area to the periphery. In 2008, the primary schools were mainly concentrated in the old urban areas within the Ming Dynasty City Wall and their clustering is obvious, with Gulou District accounted for 35.29%, while Qinhuai District and Xuanwu District respectively accounted for 28.10% and 16.34% of them. With the merger of inner-city primary schools and the splitting and expansion of peripheral primary schools, the spatial distribution of primary schools became more balanced in 2018, and the density of schools in Nanjing’s peripheral areas increased. Yet, when compared with primary schools, the overall layout of junior high schools is more balanced and stable. From 2008 to 2018, the layout of juniorhigh schools further expanded to the periphery, and the allocation rate of peripheral education resources likewise increased.

Table 2  Data characteristics of junior high schools and primary schools in the main urban area of Nanjing

 

2008

2018

 

Primary school

Junior high school

Primary school

Junior high school

Number

153

47

143

50

Total area (km2)

1.51

1.28

1.61

1.42

Senior high school entrance examination results (> 550); or Admission rate of Nanjing Foreign Language School (>10%)

Lhasa Road Primary school (16.01); Langya Road Primary school (14.05); Lixue Primary school (12.24); Jinling Huiwen Primary school (10.82); Fangcaoyuan Primary school (10.24); Yincheng Primary school (10.18)

Shuren Middle school (624.2); Xincheng Middle school (616.6); No.3 Middle school (597.6); No.29 Middle school (592.6); Kelihua Middle school (589.7); Jinling Huiwen Middle school (581.3); No.1 Middle school (579.9); Zhonghua Middle school(566.5); No.50 Middle school (563.4); No.13 Middle school (562.3); Bole Middle school (558.2); Wenchang Middle school of No.3 Middle school (554.1); No.12 Middle school (553.0); No.9 Middle school (552.0)

Langya Road Primary school (17.01); Lhasa Road Primary school (16.25); Fangcaoyuan Primary school (15.6); Beijing East Road Primary school (13.2); Lixue Primary school (12.5); Yincheng Primary school (11.34); Jinling Huiwen Primary school (10.07)

Shuren Middle school (598.9); Huangshan Road, Xincheng Middle school(577.5); No.3 Middle school (577.0); No.29 Middle school (576.8)Xincheng Middle school (572.6); Kelihua Middle school (572.4); Jiangnan Middle school of chemical plant (565.8); Jinling Huiwen Middle school (561.7); No.1 Middle school (559.5)

Note: The bracketed data in primary school is the admission rate of Nanjing foreign language school, and the bracketed data in middle school is the score of senior high school entrance examination. Due to the reform of the Nanjing mid-term exam scores in 2014, in order to facilitate data comparison, the total scores of the 2008 and 2018 mid-term exams were standardized for 700 points.

 

 

 

Figure 2  Distribution of junior high schools and primary schools in the main urban areas of Nanjing

The service scope in the inner city of primary school district is smaller than that in the peripheral area, and the accessibility of the inner-city school district is higher. The junior high school districts are larger in scale and wider in the scope of their services. From a spatial perspective, most of the residential areas are allocated to the nearest school district according to the principle of proximity to schools. However, a small number of residential areas are also allocated to school districts further away, resulting in reduced accessibility of students’ getting to school and increased transportation costs. On the whole, the service range of the peripheral schools is wider than that of the inner-city schools, but the unbalanced distribution of educational resources has evidently increased due to the outdated peripheral school facilities and the heavy load they shoulder. Changes in school-carrying pressures may therefore force the education authorities to adjust the boundaries of school districts, thus affecting the accessibility and quality of education in residential areas, which could have a profound impact on the allocation pattern of compulsory education[29].

5 Discussion and Conclusion

China’s large population places a large demand on its educational resources. On the basis of ensuring basic educational resources, we should further ensure the effectiveness and efficiency of educational resources’ allocation, aim to improve the balance of educational structure and distribution of educational resources, and strive to achieve a fair and reasonable allocation of educational resources[30]. Using available data on the school district distribution and teaching quality of primary and junior high schools in Nanjing, this compiled data set focused on analyzing the evolution characteristics of the spatial pattern of compulsory education in Nanjing, with the objective of providing new research materials and perspectives for studying the equitable allocation of urban compulsory education resources in China. The influencing factors of education inequality and imbalanced allocation of educational resources are numerous and complicated. This article only visualizes the data of primary and junior high school locations and school district ranges, expanded upon via a simple data description and spatial analysis. This was not combined, however, with considerations of the relationships among traffic accessibility, time accessibility, opportunity availability, facility capacity allocation, education quality and allocation of teaching resources, or other external factors, such as government education policies, employment distribution, and changes in housing prices around each school. On its own, the data set for Nanjing is insufficient, in that it cannot fully explain the issue of educational inequity many Chinese cities are facing. Nonetheless, this data set does provide a data basis for further research, but additional data on internal and external factors that can affect the efficiency of educational resource allocation need to be collected and collated. The focus of subsequent research should be based on these data, to elucidate the mechanisms underpinning impacts as well as spatial effects behind the imbalance in the allocation of urban education resources; to strengthen the perspective of the social space allocation model of urban education resources in China, and to better understand the differentiation on the social class and living spaces caused by an imbalance in education resources. Social issues such as differentiation provide a reference point for the government to implement a fair planning of space for educational facilities and to promote the high-quality and balanced development of compulsory education resources in China’s cities.

Author Contributions

Song Weixuan was responsible for the overall design used for the development of the data set; Tu Tangqi collected and processed the data, such as the location of primary and junior high schools and the boundaries of school districts; Chen Yanru sorted out the attribute data, analyzed the data, and wrote the data paper.

 

References

[1]     Liu Lei. Research on resource allocation efficiency of compulsory education in Anhui Province [D]. Xuzhou: China University of Mining and Technology, 2019.

[2]     Zhang Ningmeng. Research on the balanced development path of education in Henan Province [J]. Think Tank Times, 2019 (34): 131-132.

[3]     Chen Haijun. From digital transformation to big data management: Research on digital integration path of educational resources [J]. Educational Theory & Practice, 2019, 39(16): 22-26.

[4]     Liu Zhengbo. Analysis of the allocation of primary education resources under the multiple models [J]. Educational Watch, 2019, 8(14): 77-78.

[5]     Hall J. Does school district and municipality border congruence matter?[J]. Urban Studies, 2015, 54(7): 1601-1618.

[6]     Wu Q Y, Zhang X L, Waley P. Jiaoyufication: When gentrification goes to school in the Chinese inner city [J]. Urban Studies, 2016, 53(12): 3510-3526.

[7]     Wu Q Y, Zhang X L, Waley P. When Neil Smith met Pierre Bourdieu in Nanjing, China: Bringing cultural capital into rent gap theory [J]. Housing Studies, 2017, 32(5): 659-677.

[8]     Hu Siqi, Xu Jiangang, Zhang Xiang, et al. Equalization evaluation of educational facility layout based on time accessibility [J]. Planner, 2012, 28(1): 70-75.

[9]    Xie, T. T., Feng, C. C., Yang, Y. C. Research on the fairness of spatial distribution of education facilities in River Valley Cities: a case study of Lanzhou middle school [J]. Urban Development Research, 2014, 21(8): 64-67.

[10]  Terxeira J CAntunes A P A hierarchical location model for public facility planning [J]. European Journal of Operational Research, 2008, 185(1): 92-104.

[11]  Lu Xiaoxu, Lu Yuqi, Shang Zhengyong, et al. Measurement and analysis of school system scale adjustment and spatial evolution characteristics: a case study of Nanjing senior high school [J]. Geographical Sciences, 2011, 31(12): 1454-1460.

[12]  Zhao Chenxu, Shao Jing'an, Guo Yue, et al. Spatial pattern evolution characteristics and development level of rural schools in mountainous areas [J]. Geography Research, 2016, 35(3): 455-470.

[13]  Song Weixuan, Tu Tangqi, Yin Shanggang, et al. Study on the differentiation and effects of social-spatial accessibility to compulsory education resources in Nanjing [J]. Geography Research, 2019, 38(08): 2008-2026.

[14]    Taylor R G, Vasu M L, Causby J F. Integrated Planning for School and Community: The Case of Johnston County, North Carolina [J]. Interfaces, 1999, 29(1): 67-89.

[15]  Bruno G, Genovese A, Piccolo C, et al. A Location Model for the Reorganization of a School System: The Italian Case Study [J]. Procedia-Social and Behavioral Sciences, 2014, 108(3-4): 96-105.

[16]  Zhang Xuefeng. Spatial distribution analysis of rural primary and secondary schools in Gongyi City based on GIS [D]. Henan University, 2008.

[17]  Ji Yunsong. The role of geographic information system technology in the adjustment of the layout of primary and junior high schools [J]. Geospatial Information, 2006, 4(6): 62-64.

[18]  Zheng Huanhuan. Exploration of substantially fair allocation of educational resources [J]. Teaching and Management, 2018(30): 31-33.

[19] Zuo Hongmei. The basis, dilemma and transcendence of implementing school district system in compulsory education stage [J]. Education Herald, 2017(06): 35-41.

[20] Yang Wenzheng, Xiong Caiping, You Haolong, et al. Innovation of optimal allocation mechanism of performance-based digital education resources [J]. Modern Distance Education Research, 2015(06): 36-45.

[21] Chen Yanru, Tu Tangqi, Song Weixuan. Comparison data set of school district distribution of public junior high schools and primary schools in the main urban area of Nanjing (2008, 2018) [DB/OL]. Global Change Data Repository, 2019.DOI:10.3974/geodb.2019.06.12.V1.

[22] GCdataPR Editorial Office. GCdataPR Data Sharing Policy [OL]. DOI: 10.3974/dp.policy.2014.05 (Updated 2017).

[23] Nanjing Statistical Bureau. Statistical Yearbook of Nanjing (2009) [M]. Nanjing: Nanjing Press, 2009.

[24] Nanjing Municipal Education Bureau, Nanjing Planning Bureau, Nanjing Urban Planning Research Center. Planning of Primary School and Junior High School in Nanjing (2006–2020) [EB/OL]. http://www.house365.com/planinfo/record.php?threadid=130&pn=2.

[25] Harbin Map Publishing House. Traffic and Tourism Map of Nanjing City in 2008 [CM]. Harbin: Harbin Map Publishing House, 2008.

[26] Nanjing Municipal Education Bureau. Education in each district [EB/OL]. http://edu.nanjing.gov.cn/, 2019-01-10.

[27] Soxue.com. Nanjing School Directory [EB/OL]. http://www.51sxue.com/nj/, 2019-01-03.

[28] Nanjing Audio-visual Education Museum (Nanjing Education Information Center). Nanjing Education Map [EB/OL]. http://maps.nje.cn/, 2019-01-12.

[29] Tu Tangqi, Yan Dongsheng, Chen Jianglong, et al. Evolution of compulsory educational facilities in urban Nanjing [J]. Geographical Science, 2019, 39(3): 433-441.

[30]  Jiang Dongli. Research on the optimal allocation of educational resources in henan province under the background of urbanization [J]. Modern Marketing (Chuangfu Information Edition), 2018(12): 171.

 

Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum