💖💖作者：计算机毕业设计小途 💙💙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 深度学习实战项目
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基于大数据的广东省房价数据可视化分析系统介绍
《基于大数据的广东省房价数据可视化分析系统》是一套采用现代化大数据技术栈构建的房地产数据分析平台，该系统充分利用Hadoop分布式存储框架和Spark大数据处理引擎的强大计算能力，结合HDFS分布式文件系统实现海量房价数据的高效存储与处理。系统在技术架构上支持Python+Django和Java+Spring Boot双重开发方案，前端采用Vue框架配合ElementUI组件库构建现代化用户界面，通过Echarts图表库实现丰富的数据可视化效果，后端数据存储基于MySQL数据库，并深度集成Spark SQL、Pandas、NumPy等专业数据分析工具。在功能设计上，系统提供完整的用户管理体系，包括系统首页、个人信息管理、密码修改等基础功能，核心亮点在于数据大屏可视化模块，该模块集成了时间趋势分析、地理位置分析、房型价格分析、市场结构分析以及楼盘特色分析五大专业分析维度，能够从多角度深入剖析广东省房地产市场动态。系统通过大数据技术对海量房价数据进行智能化处理和分析，为用户提供直观的可视化图表和专业的数据洞察，不仅满足了房地产从业者的市场分析需求，也为政府部门制定相关政策提供了数据支撑，同时系统的技术架构体现了当前主流的大数据解决方案，具有很高的技术参考价值和实用意义。
基于大数据的广东省房价数据可视化分析系统演示视频
基于大数据的广东省房价数据可视化分析系统演示图片
基于大数据的广东省房价数据可视化分析系统代码展示
spark = SparkSession.builder.appName("GuangdongHousePriceAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_time_trend(start_date, end_date, city_name):
house_df = spark.sql("SELECT * FROM house_price_data WHERE trade_date BETWEEN '{}' AND '{}' AND city = '{}'".format(start_date, end_date, city_name))
monthly_trend = house_df.groupBy(spark.sql.functions.date_format("trade_date", "yyyy-MM").alias("month")).agg(spark.sql.functions.avg("unit_price").alias("avg_price"), spark.sql.functions.count("*").alias("transaction_count"), spark.sql.functions.max("unit_price").alias("max_price"), spark.sql.functions.min("unit_price").alias("min_price"))
monthly_trend = monthly_trend.orderBy("month")
price_change_df = monthly_trend.withColumn("prev_price", spark.sql.functions.lag("avg_price").over(Window.orderBy("month")))
price_change_df = price_change_df.withColumn("price_change_rate", ((spark.sql.functions.col("avg_price") - spark.sql.functions.col("prev_price")) / spark.sql.functions.col("prev_price") * 100))
trend_result = price_change_df.select("month", "avg_price", "transaction_count", "max_price", "min_price", "price_change_rate").collect()
trend_data = []
for row in trend_result:
month_data = {"month": row["month"], "average_price": round(row["avg_price"], 2), "transaction_volume": row["transaction_count"], "highest_price": row["max_price"], "lowest_price": row["min_price"], "growth_rate": round(row["price_change_rate"] if row["price_change_rate"] else 0, 2)}
trend_data.append(month_data)
overall_trend = "上涨" if len(trend_data) > 1 and trend_data[-1]["average_price"] > trend_data[0]["average_price"] else "下跌"
avg_growth_rate = sum([item["growth_rate"] for item in trend_data if item["growth_rate"]]) / len([item for item in trend_data if item["growth_rate"]])
return {"trend_direction": overall_trend, "monthly_data": trend_data, "average_growth_rate": round(avg_growth_rate, 2), "analysis_period": "{} 至 {}".format(start_date, end_date)}
def analyze_geographical_distribution(province="广东省"):
geo_df = spark.sql("SELECT city, district, latitude, longitude, unit_price, house_area, property_type FROM house_price_data WHERE province = '{}'".format(province))
city_stats = geo_df.groupBy("city").agg(spark.sql.functions.avg("unit_price").alias("avg_city_price"), spark.sql.functions.count("*").alias("city_count"), spark.sql.functions.avg("house_area").alias("avg_area"))
district_stats = geo_df.groupBy("city", "district").agg(spark.sql.functions.avg("unit_price").alias("avg_district_price"), spark.sql.functions.count("*").alias("district_count"))
city_ranking = city_stats.orderBy(spark.sql.functions.desc("avg_city_price"))
top_cities = city_ranking.limit(10).collect()
district_ranking = district_stats.orderBy(spark.sql.functions.desc("avg_district_price"))
top_districts = district_ranking.limit(20).collect()
price_distribution = geo_df.select("latitude", "longitude", "unit_price", "city", "district").collect()
geo_result = {"city_ranking": [], "district_ranking": [], "map_data": []}
for city in top_cities:
city_info = {"city_name": city["city"], "average_price": round(city["avg_city_price"], 2), "property_count": city["city_count"], "average_area": round(city["avg_area"], 2)}
geo_result["city_ranking"].append(city_info)
for district in top_districts:
district_info = {"city": district["city"], "district_name": district["district"], "average_price": round(district["avg_district_price"], 2), "property_count": district["district_count"]}
geo_result["district_ranking"].append(district_info)
for location in price_distribution:
if location["latitude"] and location["longitude"]:
map_point = {"lat": location["latitude"], "lng": location["longitude"], "price": location["unit_price"], "city": location["city"], "district": location["district"]}
geo_result["map_data"].append(map_point)
return geo_result
def analyze_house_type_pricing(property_types=None):
if property_types is None:
property_types = ["住宅", "公寓", "别墅", "商铺"]
type_filter = " OR ".join(["property_type = '{}'".format(pt) for pt in property_types])
house_type_df = spark.sql("SELECT property_type, unit_price, house_area, total_price, room_count, hall_count FROM house_price_data WHERE {}".format(type_filter))
type_analysis = house_type_df.groupBy("property_type").agg(spark.sql.functions.avg("unit_price").alias("avg_unit_price"), spark.sql.functions.avg("house_area").alias("avg_area"), spark.sql.functions.avg("total_price").alias("avg_total_price"), spark.sql.functions.count("*").alias("sample_count"), spark.sql.functions.max("unit_price").alias("max_unit_price"), spark.sql.functions.min("unit_price").alias("min_unit_price"))
room_analysis = house_type_df.filter(house_type_df.room_count.isNotNull()).groupBy("room_count", "hall_count").agg(spark.sql.functions.avg("unit_price").alias("avg_price_by_room"), spark.sql.functions.count("*").alias("room_sample_count"))
area_ranges = [(0, 50), (50, 90), (90, 120), (120, 150), (150, 200), (200, 1000)]
area_analysis_data = []
for min_area, max_area in area_ranges:
area_df = house_type_df.filter((house_type_df.house_area >= min_area) & (house_type_df.house_area < max_area))
area_stats = area_df.agg(spark.sql.functions.avg("unit_price").alias("avg_price"), spark.sql.functions.count("*").alias("count")).collect()[0]
if area_stats["count"] > 0:
area_range_str = "{}㎡-{}㎡".format(min_area, max_area if max_area != 1000 else "以上")
area_analysis_data.append({"area_range": area_range_str, "average_price": round(area_stats["avg_price"], 2), "sample_count": area_stats["count"]})
type_result = type_analysis.collect()
room_result = room_analysis.orderBy("room_count", "hall_count").collect()
pricing_analysis = {"property_type_analysis": [], "room_type_analysis": [], "area_analysis": area_analysis_data}
for type_row in type_result:
type_data = {"property_type": type_row["property_type"], "average_unit_price": round(type_row["avg_unit_price"], 2), "average_area": round(type_row["avg_area"], 2), "average_total_price": round(type_row["avg_total_price"], 2), "sample_count": type_row["sample_count"], "price_range": {"max": type_row["max_unit_price"], "min": type_row["min_unit_price"]}}
pricing_analysis["property_type_analysis"].append(type_data)
for room_row in room_result:
room_data = {"room_config": "{}室{}厅".format(room_row["room_count"], room_row["hall_count"]), "average_price": round(room_row["avg_price_by_room"], 2), "sample_count": room_row["room_sample_count"]}
pricing_analysis["room_type_analysis"].append(room_data)
return pricing_analysis
基于大数据的广东省房价数据可视化分析系统文档展示
💖💖作者：计算机毕业设计小途 💙💙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 深度学习实战项目
