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Background and Significance
The research background and significance of the large-screen full-screen system for visual analysis of second-hand housing crawler data in Shanghai using Python are as follows:
Research Background:
The status of Shanghai’s real estate market: As the economic center of China, Shanghai’s real estate market plays an important role domestically and even globally. As an important part of Shanghai's real estate market, the second-hand housing market has considerable activity and market size.
Challenges of data growth: With the popularization of the Internet and big data technology, the amount of data on Shanghai’s second-hand housing listings has increased dramatically, making it increasingly difficult to manually process and analyze this data.
Development of technology and tools: The Python programming language and its related crawlers and data visualization technologies provide effective means for processing and analyzing large-scale data.
Significance:
Improve market efficiency: Through Python crawler technology, data on second-hand housing listings in Shanghai can be quickly and automatically obtained, which greatly improves the efficiency of data acquisition and reduces labor costs.
Enhance data transparency: Through data visualization analysis, the dynamics and trends of Shanghai's second-hand housing market can be displayed more intuitively and comprehensively, enhancing market transparency and reducing information asymmetry.
Assisted decision-making: For investors, home buyers, policy makers, etc., Python-based visual analysis of second-hand housing crawler data can provide more accurate and timely market information and provide data support for decision-making.
Promote technological innovation and application: Research and practice Python Shanghai second-hand housing crawler data visual analysis large-screen full-screen system can promote technological innovation and application expansion in related fields, and inject new vitality into the development of the real estate industry and big data technology.
In summary, this research has obvious practical significance and market application value in the field of real estate data analysis. It also provides new research and application scenarios for the development of related technologies and tools.
Status quo at home and abroad
The domestic and foreign research status of Python Shanghai second-hand housing crawler data visualization analysis large-screen full-screen system is as follows:
Research state in China:
Domestically, there is an increasing demand for crawling and analyzing second-hand housing data, especially in first-tier cities like Shanghai. At present, some teams and companies have used Python to crawl Shanghai's second-hand housing data, and have achieved preliminary results. They use Python's crawler libraries, such as Scrapy and BeautifulSoup, to crawl second-hand housing information from major real estate websites, and clean and organize the data.
In terms of data visualization, domestic research mainly focuses on traditional chart displays, such as bar charts, line charts, and pie charts. Although these charts can display some basic statistical information, they still have certain limitations for a comprehensive and in-depth understanding of the second-hand housing market. In addition, there is relatively little domestic research on large-screen display of second-hand housing data, and there are still some technical challenges that need to be overcome, such as real-time data updating and interactivity.
Current status of foreign research:
In contrast, foreign research on large-screen full-screen systems for visual analysis of Python second-hand housing crawler data is more mature. They not only possess advanced crawler technology and data processing methods, but also focus on combining data analysis with business practices to develop application systems with more practical and commercial value.
In terms of data visualization, foreign research pays more attention to innovation and interactivity, trying to use various novel visualization technologies and tools to display second-hand housing market data. For example, some foreign research teams use large-screen full-screen systems to display real-time data and analysis results on the second-hand housing market, and present market dynamics and trends through dynamic charts, maps, heat maps, etc. These visualization methods not only provide a more intuitive and comprehensive display of information, but also enhance the interactive experience between users and data.
In summary, there is a certain research foundation and practical experience at home and abroad in the field of Python Shanghai second-hand housing crawler data visualization analysis large-screen full-screen system. However, there are still some shortcomings and challenges in domestic research that need to be overcome, such as technological innovation and data integrity that need to be further improved. At the same time, we can learn from some foreign advanced technologies and practical experiences to promote research and application development in this field.
Feature list
Here we analyze the system content we intend to implement as follows, the data source is Lianjia
Large screen full screen visual display:
- Basic data on second-hand houses: the total number of houses, the total number of communities, the average area of the houses, the average price of the houses
- Average sales data of second-hand houses in various regions (bar chart)
- Average area of properties in each region (line chart)
- The innovative point is that in regional areas, the number of housing listings is displayed by each area.
- The number of communities and houses in each area, displayed in a double column chart
- Analysis of the proportion of units in each area: less than 89 square meters, 90 to 149 square meters, 150-199 square meters, and more than 200 square meters
- Latest housing data, scrolling display of the latest 10 housing information
Background content:
- Administrator login, password change, and system exit
- Display all property data and link to original address
- Regional data list: displays sales data in each district, including number of houses, average area, average price, etc.
Community data list: Displays the area where each community is located, the number of properties in the community, the average price and area of the properties in the community, etc.
Interface renderings
Backend functions
