💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
基于大数据的中国火车站站点地理数据分析系统介绍
《中国火车站站点地理数据分析系统》是一个基于大数据技术栈构建的综合性地理信息分析平台，采用Hadoop分布式存储架构和Spark大数据处理引擎作为核心技术支撑。系统运用Python语言结合Django框架搭建后端服务，前端采用Vue.js配合ElementUI组件库和Echarts可视化工具构建用户交互界面。在数据处理层面，系统深度整合Spark SQL进行结构化数据查询分析，利用Pandas和NumPy进行数据清洗和统计计算，通过HDFS实现海量地理数据的可靠存储。系统核心功能涵盖火车站站点信息管理、多维度数据可视化分析、大屏展示、站点宏观特征分析、空间分布分析、铁路局管辖范围分析、站点等级划分分析以及核心站点聚集区域识别等模块。整个系统架构采用前后端分离设计模式，后端提供RESTful API接口服务，前端通过Ajax异步请求获取数据并进行动态渲染，实现了从数据采集、存储、处理到可视化展示的完整数据分析流程，为铁路交通地理信息的深度挖掘和智能决策提供了有力的技术支撑平台。
基于大数据的中国火车站站点地理数据分析系统演示视频
基于大数据的中国火车站站点地理数据分析系统演示图片
基于大数据的中国火车站站点地理数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, max, min, desc, asc, when
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("TrainStationAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def station_spatial_distribution_analysis(request):
station_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/train_stations/station_data.csv")
station_df = station_df.withColumn("longitude", col("longitude").cast(DoubleType()))
station_df = station_df.withColumn("latitude", col("latitude").cast(DoubleType()))
station_df = station_df.withColumn("station_level", col("station_level").cast(IntegerType()))
province_distribution = station_df.groupBy("province").agg(count("station_id").alias("station_count"), avg("longitude").alias("avg_longitude"), avg("latitude").alias("avg_latitude")).orderBy(desc("station_count"))
city_distribution = station_df.groupBy("province", "city").agg(count("station_id").alias("station_count")).orderBy(desc("station_count"))
level_distribution = station_df.groupBy("station_level").agg(count("station_id").alias("count")).orderBy("station_level")
longitude_stats = station_df.agg(min("longitude").alias("min_lng"), max("longitude").alias("max_lng"), avg("longitude").alias("avg_lng")).collect()[0]
latitude_stats = station_df.agg(min("latitude").alias("min_lat"), max("latitude").alias("max_lat"), avg("latitude").alias("avg_lat")).collect()[0]
density_analysis = station_df.withColumn("lng_zone", (col("longitude") / 2).cast(IntegerType()) * 2).withColumn("lat_zone", (col("latitude") / 2).cast(IntegerType()) * 2).groupBy("lng_zone", "lat_zone").agg(count("station_id").alias("density_count")).orderBy(desc("density_count"))
high_density_zones = density_analysis.filter(col("density_count") > 10).select("lng_zone", "lat_zone", "density_count")
regional_distribution = station_df.withColumn("region", when(col("longitude") < 110, "西部").when(col("longitude") < 120, "中部").otherwise("东部")).groupBy("region").agg(count("station_id").alias("region_count"), avg("station_level").alias("avg_level"))
province_pandas = province_distribution.toPandas()
city_pandas = city_distribution.toPandas()
level_pandas = level_distribution.toPandas()
density_pandas = high_density_zones.toPandas()
regional_pandas = regional_distribution.toPandas()
result_data = {"province_distribution": province_pandas.to_dict('records'), "city_distribution": city_pandas.to_dict('records'), "level_distribution": level_pandas.to_dict('records'), "coordinate_stats": {"longitude": {"min": longitude_stats["min_lng"], "max": longitude_stats["max_lng"], "avg": longitude_stats["avg_lng"]}, "latitude": {"min": latitude_stats["min_lat"], "max": latitude_stats["max_lat"], "avg": latitude_stats["avg_lat"]}}, "high_density_zones": density_pandas.to_dict('records'), "regional_distribution": regional_pandas.to_dict('records')}
return JsonResponse({"status": "success", "data": result_data})
def railway_bureau_jurisdiction_analysis(request):
bureau_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/train_stations/bureau_data.csv")
station_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/train_stations/station_data.csv")
joined_df = station_df.join(bureau_df, station_df.bureau_code == bureau_df.bureau_code, "left")
bureau_stats = joined_df.groupBy("bureau_name", "bureau_code").agg(count("station_id").alias("total_stations"), sum(when(col("station_level") == 1, 1).otherwise(0)).alias("level1_count"), sum(when(col("station_level") == 2, 1).otherwise(0)).alias("level2_count"), sum(when(col("station_level") == 3, 1).otherwise(0)).alias("level3_count"), sum(when(col("station_level") == 4, 1).otherwise(0)).alias("level4_count"), avg("longitude").alias("center_longitude"), avg("latitude").alias("center_latitude")).orderBy(desc("total_stations"))
coverage_analysis = joined_df.groupBy("bureau_name").agg(count("province").alias("province_coverage"), count("city").alias("city_coverage"), min("longitude").alias("west_boundary"), max("longitude").alias("east_boundary"), min("latitude").alias("south_boundary"), max("latitude").alias("north_boundary"))
bureau_efficiency = bureau_stats.withColumn("high_level_ratio", (col("level1_count") + col("level2_count")) / col("total_stations")).withColumn("coverage_area", (col("center_longitude") * col("center_latitude")) / 100).select("bureau_name", "total_stations", "high_level_ratio", "coverage_area").orderBy(desc("high_level_ratio"))
province_bureau_mapping = joined_df.groupBy("province", "bureau_name").agg(count("station_id").alias("station_count")).orderBy("province", desc("station_count"))
major_bureaus = bureau_stats.filter(col("total_stations") > 100).select("bureau_name", "total_stations", "level1_count", "level2_count")
geographic_span = coverage_analysis.withColumn("longitude_span", col("east_boundary") - col("west_boundary")).withColumn("latitude_span", col("north_boundary") - col("south_boundary")).select("bureau_name", "longitude_span", "latitude_span", "province_coverage", "city_coverage").orderBy(desc("longitude_span"))
bureau_pandas = bureau_stats.toPandas()
coverage_pandas = coverage_analysis.toPandas()
efficiency_pandas = bureau_efficiency.toPandas()
mapping_pandas = province_bureau_mapping.toPandas()
major_pandas = major_bureaus.toPandas()
span_pandas = geographic_span.toPandas()
result_data = {"bureau_statistics": bureau_pandas.to_dict('records'), "coverage_analysis": coverage_pandas.to_dict('records'), "efficiency_ranking": efficiency_pandas.to_dict('records'), "province_bureau_mapping": mapping_pandas.to_dict('records'), "major_bureaus": major_pandas.to_dict('records'), "geographic_span": span_pandas.to_dict('records')}
return JsonResponse({"status": "success", "data": result_data})
def core_station_cluster_analysis(request):
station_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/train_stations/station_data.csv")
traffic_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/train_stations/traffic_data.csv")
station_df = station_df.withColumn("longitude", col("longitude").cast(DoubleType()))
station_df = station_df.withColumn("latitude", col("latitude").cast(DoubleType()))
station_df = station_df.withColumn("station_level", col("station_level").cast(IntegerType()))
traffic_df = traffic_df.withColumn("daily_passengers", col("daily_passengers").cast(IntegerType()))
traffic_df = traffic_df.withColumn("train_frequency", col("train_frequency").cast(IntegerType()))
enriched_df = station_df.join(traffic_df, "station_id", "left")
core_stations = enriched_df.filter((col("station_level") <= 2) | (col("daily_passengers") > 10000) | (col("train_frequency") > 50))
cluster_grid = core_stations.withColumn("grid_x", (col("longitude") / 0.5).cast(IntegerType())).withColumn("grid_y", (col("latitude") / 0.5).cast(IntegerType()))
cluster_stats = cluster_grid.groupBy("grid_x", "grid_y").agg(count("station_id").alias("cluster_size"), avg("longitude").alias("cluster_center_lng"), avg("latitude").alias("cluster_center_lat"), sum("daily_passengers").alias("total_passengers"), avg("train_frequency").alias("avg_frequency"), min("station_level").alias("highest_level")).filter(col("cluster_size") >= 3).orderBy(desc("cluster_size"))
major_clusters = cluster_stats.filter(col("cluster_size") >= 5).withColumn("importance_score", col("total_passengers") * col("avg_frequency") / col("highest_level")).orderBy(desc("importance_score"))
city_clusters = enriched_df.groupBy("city").agg(count("station_id").alias("city_station_count"), sum("daily_passengers").alias("city_total_passengers"), avg("longitude").alias("city_center_lng"), avg("latitude").alias("city_center_lat")).filter(col("city_station_count") >= 2).orderBy(desc("city_total_passengers"))
hub_analysis = core_stations.withColumn("hub_score", when(col("station_level") == 1, 100).when(col("station_level") == 2, 80).otherwise(60) + (col("daily_passengers") / 1000) + (col("train_frequency") / 10)).select("station_name", "city", "province", "longitude", "latitude", "hub_score", "station_level", "daily_passengers").orderBy(desc("hub_score"))
top_hubs = hub_analysis.limit(20)
regional_clusters = enriched_df.withColumn("region", when(col("longitude") < 110, "西部").when(col("longitude") < 120, "中部").otherwise("东部")).groupBy("region").agg(count("station_id").alias("region_stations"), sum("daily_passengers").alias("region_passengers"), avg("train_frequency").alias("region_avg_frequency")).orderBy(desc("region_passengers"))
cluster_pandas = cluster_stats.toPandas()
major_pandas = major_clusters.toPandas()
city_pandas = city_clusters.toPandas()
hub_pandas = top_hubs.toPandas()
regional_pandas = regional_clusters.toPandas()
result_data = {"cluster_analysis": cluster_pandas.to_dict('records'), "major_clusters": major_pandas.to_dict('records'), "city_clusters": city_pandas.to_dict('records'), "top_hub_stations": hub_pandas.to_dict('records'), "regional_distribution": regional_pandas.to_dict('records')}
return JsonResponse({"status": "success", "data": result_data})
基于大数据的中国火车站站点地理数据分析系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
