学生及学区规划分析数据

本数据通过将学校的地址信息、学生的户籍地信息、住址信息等数据进行标准化清洗后，利用地址标准化工具组件，将地址信息转换成经纬度坐标点位信息数据，构建学区匹配模型、学位预测模型，得到学生与学校的科学匹配，实现动态学区规划、学位预测、饱和度预警等能力，帮助教育部门根据学生地址、家庭信息及学校地址，划定学区范围，确定学生所属学校；识别新入学预估数量，判断学位数量，帮助评估学生的入学压力，新入学人数多，学生同校竞争激烈，教学压力大；根据学校学位的饱和度判断是否需要扩建学校、调整招生政策、重新划分学区等，若饱和度大于100%，代表学位已不能满足该学区的需求，需要重新划分学区、增加学位等。本数据为教育部门分析预测入学、学区划分、教育资源分配等工作提供数据支撑。1、建立学区匹配模型、学位预测模型，（1）基于学区范围、户籍、房产、政策等信息，检索儿童和家庭关系的家庭树，形成适龄儿童直属亲属的房产信息数据清单；（2）依托户口簿、不动产等数据进行对学生进行区分；（3）利用地址标准化工具组件，将地址信息转换成经纬度坐标点位；（4）学生的适龄入学计算转换成对应年级；（5）对主城区内的所有幼儿园、小学、初中，按照就读规则，将户籍地址、产权地址、学生年级、学校地址数据圈画比对输出各学校学区适龄儿童学生清单。2、数据中前17个字段为适龄学生的年龄、户籍、房产等信息，使用学区匹配模型，得到该学生应当就读的学校，最终，学位预测模型得出该学校的学生数、学位数新入学预估、饱和度等数据，其中所有数量单位均为“个”，3、根据主城区主城区内的所有幼儿园、小学、初中的学位数、学生数，计算学校饱和度，饱和度=学生数/学位数*100，饱和度为百分比，单位为“%”。

This dataset is constructed by first performing standardized cleaning on data including school addresses, students' household registration addresses and residential addresses, then converting the address information into longitude and latitude coordinate point data using an address standardization tool component. School district matching models and enrollment quota prediction models are built to achieve scientific matching between students and schools, realizing capabilities such as dynamic school district planning, enrollment quota prediction and saturation early warning. This helps education authorities demarcate school district boundaries and determine the schools that students should attend based on students' addresses, family information and school addresses; identify the estimated number of new enrollments, judge the number of school seats, and assess the enrollment pressure on students—when the number of new enrollments is high, students face fierce competition in the same school and face greater teaching pressure; judge whether it is necessary to expand schools, adjust enrollment policies or redraw school districts based on the saturation rate of school seats. If the saturation rate exceeds 100%, it means that the school seats cannot meet the demand of the school district, and measures such as redrawing school districts and increasing school seats are required. This dataset provides data support for education authorities to analyze and predict enrollment, school district demarcation, educational resource allocation and other work.

1. Establishment of school district matching models and enrollment quota prediction models:
(1) Retrieve the immediate family tree of children and their families based on information such as school district boundaries, household registration, real estate and policies, to form a list of property information data of the immediate relatives of school-aged children;
(2) Distinguish students based on data such as household registration booklets and real estate information;
(3) Use the address standardization tool component to convert address information into longitude and latitude coordinate points;
(4) Convert the school-age enrollment calculation of students into the corresponding grade level;
(5) For all kindergartens, primary schools and junior high schools in the main urban area, conduct spatial comparison and demarcation based on enrollment rules, household registration addresses, property right addresses, student grades and school address data, and output the list of school-aged children assigned to each school district.

2. The first 17 fields in the dataset are information such as the age, household registration and property of school-aged students. Using the school district matching model, the school that the student should attend is determined. Finally, the enrollment quota prediction model generates data such as the number of students, number of school seats, new enrollment forecast and saturation rate of the school. All quantity units are "units".
3. Calculate the school saturation rate based on the number of school seats and students of all kindergartens, primary schools and junior high schools in the main urban area. The formula is: saturation rate = number of students / number of school seats * 100%, and the unit of saturation rate is "%".

搜集汇总

数据集介绍

特点

该数据集为教育行业的公共数据，包含1002条记录，每半年更新一次，主要用于学区匹配、学位预测和饱和度预警。数据通过标准化清洗和模型构建，支持教育部门进行学区规划、学位分配和资源优化。