Profile of CDA Certificate‑Holder

Yang Zhenxi, CDA Level‑I Certificate Holder, Master’s Candidate in Information Science at Zhengzhou University, Data Analyst of a listed company.

I. Overview of Smart City Digital Applications

To understand smart cities, two key points need to be clarified.

What is a Smart City?

A smart city is not a single industry or domain, but a comprehensive concept. It promotes multi‑dimensional coordinated urban development through technology integration and application. It mainly consists of four layers:

• Perception Layer: Responsible for data collection. Smart cities gather urban data via sensors, cameras and other devices.
• Communication Layer: Responsible for data transmission, mainly including 5G and the Internet.
• Platform Layer: Built on big‑data centers for data observation and prediction, including the construction of relevant models and algorithms.
• Application Layer: Covers functional collaborations such as intelligent transportation and digital government. It represents cross‑integration of multiple industries including hardware manufacturing, software development and system integration. It mainly consolidates data resources and diverse resources, innovates application modes, and advances improvements in urban governance and residents’ quality of life.

As of November 2024, 243 provincial‑level and municipal‑level local governments in China have launched platforms. These platforms open public data such as API interfaces and statistical yearbooks, including 24 provincial‑level platforms and approximately 219 municipal‑level platforms.

Application Value of Smart City Solutions

The application value of data analytics in smart cities is reflected in the following aspects:

1. Data‑driven decision‑making: Massive datasets deliver more scientific decision‑making references for urban administrators.
2. Prediction and early warning: Data analytics enables smart cities to deliver advance predictions and early warnings for scenarios such as traffic congestion and natural disasters including earthquakes.
3. Personalized service delivery: Smart cities identify public demands and deliver personalized public services such as intelligent navigation and customized medical advice to improve quality of life. For example, citizens dial the 12345 hotline for inquiries on tax affairs and housing‑related disputes.
4. Optimized resource allocation: Comprehensive data analysis of urban resources optimizes allocation of public resources including water, electricity and gas, improves resource efficiency and reduces waste.

II. Intelligent Urban Information Collection

From a data‑analytics perspective, urban information collection corresponds to the data‑gathering phase of analytical workflows.

Main collection methods for multi‑source heterogeneous urban data:

1. Multi‑sensor fusion: Cameras, infrared sensors and other perception devices conduct full‑scale real‑time data collection covering urban traffic, environment and energy sectors to guarantee comprehensiveness and timeliness.
2. UAV inspection: Scheduled or emergency patrols capture aerial imagery and video data along preset flight routes, compensating for limitations of ground‑based observation. Suitable for high‑risk scenarios such as mud‑rock flows where human personnel, fixed cameras or vehicle patrols are impractical.
3. Satellite remote sensing: Acquires large‑scale urban geospatial information. The GIS geographic‑information system performs spatial analysis to pinpoint locations, supporting the generation of location‑based heatmaps and macro environmental‑monitoring datasets.
4. Smart facilities: For instance, smart street‑lamps deployed along roadways equipped with energy‑monitoring terminals collect real‑time road condition data to underpin urban energy management. Smart street‑lamps integrate video surveillance, digital displays, traffic‑flow monitoring and noise‑environment sensing, alongside adaptive smart lighting that switches on and off according to ambient brightness.

After raw data collection, data cleansing and preprocessing are required.

There are multiple intelligent data‑acquisition approaches. For example, UAVs perform scheduled or emergency low‑altitude urban inspections to obtain high‑resolution images and video footage.

High‑quality intelligent data collection lays the foundation for continuous monitoring, adjustment and optimization of urban living systems.

III. Intelligent Analysis of Citizen Hotline Appeals

This section defines service domains covered by citizen hotline data analytics, alongside relevant analytical methodologies, supporting tools and knowledge‑graph construction approaches.

Taking sentiment word‑cloud visualization as an example, below are step‑by‑step procedures for text sentiment analysis:

1. Download relevant datasets containing positive‑word and negative‑word tables. Count word occurrences and record volumes to assess overall public sentiment tendencies.
2. Import the part‑of‑speech editor. Import keywords from Excel files and preview word‑frequency statistics. Multiple built‑in templates are available.
3. Reformat datasets to meet data‑processing specifications.
4. Upload pre‑processed positive‑sentiment datasets in Excel format.

Upon upload, the tool automatically processes input data and generates positive‑sentiment word clouds, negative‑sentiment word clouds and composite word‑cloud outputs containing both positive and negative vocabulary. Visual outputs deliver intuitive insights into overall public sentiment tendencies.