Method for Detecting Urban Disaster Resilience Threshold Based on Location #BigData https://www.youtube.com/watch?v=H7JU3Dsdfi8

The invention you described is an innovative approach to detecting urban disaster resilience thresholds using location big data.

1. Data Input:

- Historical or Real-Time Positioning Request Data: This data serves as the primary input, capturing the movement and activity patterns of people within a city.

2. Stable Human Activity Area Extraction:

- Range Identification: The system identifies the stable human activity areas within the target city. This involves analyzing where people consistently move and interact.

3. Rainfall Event Detection:

- Event Extraction: Based on the identified human activity areas, the system extracts all rainfall events occurring in the city.

4. Human Activity Response Analysis:

- Grid-Level Analysis: The system examines how human activity changes in response to rainfall events at a granular level (grid-by-grid).

- Urban-Scale Analysis: It also looks at the overall city-wide response to these events.

5. Threshold Determination:

- Resilience Threshold Identification: The system determines if a rainfall event triggers the urban disaster resilience threshold. If it does, the event is marked as Y0; otherwise, it is marked as non-Y0.

6. Public Perception Calculation:

- Threshold Perception: The system calculates public perception of the disaster resilience threshold based on the differences in rainfall characteristics between Y0 and non-Y0 events.

7. Alarm System:

- Event-Based Alerts: If a rainfall event exceeds the city's disaster resilience threshold, an alarm message is issued to alert relevant authorities and the public.

Benefits:

1. Localized Early Warning Standards:

- The invention helps establish disaster resilience threshold early warning standards tailored to local conditions, making the system highly adaptable to different urban environments.

2. Enhanced Emergency Prevention:

- By improving the ability to predict and respond to extreme rainfall disasters, the system enhances the overall emergency prevention capabilities of a city.

3. Automated Detection and Alerting:

- The automated nature of the system allows for real-time detection and alerting, which can significantly reduce the time needed to respond to potential disasters.

4. Reduction of Human and Material Loss:

- Through accurate and timely alerts, the system can help avoid or mitigate the human and material losses associated with extreme rainfall events.

Potential Applications:

- Urban Planning: Helps urban planners design more resilient cities by understanding how different areas respond to extreme weather events.

- Emergency Management: Provides emergency response teams with real-time data and alerts, enabling faster and more effective responses.

- Public Safety: Enhances public safety by keeping the population informed and prepared for potential disasters.

This invention represents a significant advancement in urban disaster management by leveraging big data and advanced analytics to predict and respond to extreme weather events. It not only improves the resilience of cities but also enhances the overall safety and well-being of urban populations.

This patent outlines a comprehensive method and system for detecting urban disaster resilience thresholds using location big data.

Claim 1: Method Overview

1. Step 1: Input historical or real-time positioning request data to extract the range of the human activity stable zone and identify all rainfall events within this zone.

2. Step 2: Extract human activity behavior responses at both the grid and city scales, and determine if a rainfall event triggers the urban disaster resilience threshold. Mark events as Y0 (threshold triggered) or non-Y0 (threshold not triggered).

3. Step 3: Calculate the public’s perceived disaster resilience threshold based on differences in rainfall characteristics between Y0 and non-Y0 events. Issue an alarm if a rainfall event exceeds the threshold.

Claim 2: Detailed Sub-Steps for Step 1

1. Step 1.1: Identify stable human activity areas by extracting grids with hourly positioning request averages exceeding a preset number.

2. Step 1.2: Resample the precipitation raster dataset to match the spatiotemporal resolution of the positioning request data. Calculate hourly rainfall within the stable human activity area to form an urban hourly rainfall time series and extract all rainfall events.

Claim 3: Human Activity BehavioUr Response Extraction

- Normalize the positioning request data time series for each grid within the stable human activity area.

- Detect positive and negative anomalies in the positioning request data time series.

### Claim 4: Urban-Scale Human Activity Response Extraction

- Count positive and negative anomaly grids hourly to form time series (Rainfall PTLR/NTLR).

- Calculate the median of these values during rainless periods to obtain normal activity levels (Normal PTLR/NTLR).

Claim 5: Determining Threshold Triggering

- Extract subsequences of Rainfall PTLR and Normal PTLR during rainfall events.

- Use statistical tests to compare means of these subsequences. If there’s a significant difference, mark the event as Y0 (threshold triggered); otherwise, mark it as non-Y0.

1. Step 3.1: Extract rainfall index (Ri) and rainfall time index (Rt) during Y0 and non-Y0 periods.

2. Step 3.2: Construct a linear binary classifier to determine the rainfall threshold (R_crit). If observed rainfall index (I_e) exceeds R_crit, the event is considered to exceed the city’s disaster resilience threshold.

Claim 7: Specific Indices

- Use hourly rainfall as the rainfall index (Ri) and rainfall duration as the rainfall time index (Rt).

Claim 8: Alarm Notification

- Automatically issue an alarm notification if an event exceeds the urban disaster resilience threshold.

- A system designed to implement the method described in claims 1-8.

Claim 10: System Modules

1. First Module: Extracts the human activity stable zone and rainfall events.

2. Second Module: Extracts human activity behavior responses and determines threshold triggering.

3. Third Module: Calculates the public’s perceived disaster resilience threshold and issues alarms.

This patent provides a robust framework for detecting urban disaster resilience thresholds using location big data. By analyzing human activity patterns and rainfall events, it enables the identification of critical thresholds and the issuance of timely alarms. This system enhances urban resilience and emergency response capabilities, ultimately reducing the impact of extreme weather events on urban populations.

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