Lightning risk indexes identifying the potential number of dangerous lightning events (NDLE) and ground sensitivity to lightning in residential subdistricts in the Beijing metropolitan area have been estimated on a 5 m resolution grid for the first time. The gridded cloud-to-ground (CG) lightning strike density was used in the NDLE calculation, on account of the multiple contacts formed by CG events with multiple lightning flashes. Meanwhile, in the NDLE estimates, the critical CG strike densities derived from the lightning location system data were corrected for network detection efficiency (DE). The case study for a residential sub-district indicates that the site-specific sensitivity to lightning, which is determined by the terrain factors related to lightning attachment and the lightning rod effects induced by nearby structures, differs greatly among types of underlying ground areas. The discrepancy in the NDLE, which is the numerical product of sensitivity and CG strike density, is dominated by the sensitivity to the relatively stationary CG strike density at the residential sub-district scale. Conclusively, the visualization of lightning risk sensitivity and NDLE differences in parts of a residential sub-district at a high spatial resolution makes this model useful in risk reduction and risk control for lightning risk management. KEYWORDS Lightning risk; CG flash density; lightning detection efficiency CONTACT HaiBo Hu hbhu@ium.cn

<p>Visualization of lightning location data is a necessary step in analyzing and researching lightning activity patterns. This article uses C# and the cross-platform .NET framework to develop a lightning location data analysis class library and the data-driven client to help lightning researchers improve work efficiency and avoid repeated wheel invention. Lightning Location System Data Analyzer (LLSDA) is a suite of software tools that includes a .NET class library for software developers and a desktop application for end users. LLSDA supports a wide range of lightning location data formats, such as the University of Washington (University of Washington) Global Lightning Location System (WWLLN) and Beijing Huayun Dongfang ADTD Lightning Location System data format, and maintains scalability, allowing the addition of other lightning location data Format. The class library can easily read, parse, and analyze lightning location data, and combined with third-party frameworks can realize grid analysis. The desktop application can be combined with MeteoInfo (a geographic information system open-source project) for secondary development. It has a good GUI and is an open-source tool suite for viewing and visualizing lightning location system datasets.  </p>

<p>Visualization of lightning location data is a necessary step in analyzing and researching lightning activity patterns. This article uses C# and the cross-platform .NET framework to develop a lightning location data analysis class library and the data-driven client to help lightning researchers improve work efficiency and avoid repeated wheel invention. Lightning Location System Data Analyzer (LLSDA) is a suite of software tools that includes a .NET class library for software developers and a desktop application for end users. LLSDA supports a wide range of lightning location data formats, such as the University of Washington (University of Washington) Global Lightning Location System (WWLLN) and Beijing Huayun Dongfang ADTD Lightning Location System data format, and maintains scalability, allowing the addition of other lightning location data Format. The class library can easily read, parse, and analyze lightning location data, and combined with third-party frameworks can realize grid analysis. The desktop application can be combined with MeteoInfo (a geographic information system open-source project) for secondary development. It has a good GUI and is an open-source tool suite for viewing and visualizing lightning location system datasets.  </p>