Developing an Automated Analytical Process for Disaster Response and Recovery in Communities Prone to Isolation
Abstract
:1. Introduction
1.1. Background
- (1)
- What are the essential geospatial technologies that can be used to help people living in communities that are vulnerable in the event of a disaster?
- (2)
- Why do we need an analytical process to identify and analyze isolated communities in real disaster situations, and what analytical procedures are required for the communities that are prone to isolation?
- (3)
- What kind of systems can disaster managers use to deliver information relevant in isolated areas to citizens in real time, and how can they efficiently operate them?
- (4)
- Can we develop scenario-based approaches on web maps to rapidly help isolated communities at risk of disasters?
1.2. Previous Studies
2. Materials and Methods
2.1. Materials
2.1.1. Study Area
2.1.2. Data Collection
2.2. Methods
2.2.1. Schematic Diagram for this Study
2.2.2. Development of the Three Modules
- First module: Extracting isolated communities affected by disaster
- B.
- Second module: Analytical procedures for disaster response
- C.
- Third module: Developing a web-enabled GIS application
3. Results
3.1. Real Application with Three Communities where Disasters Have Occurred in the Past
3.2. Web-Enabled GIS Application
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Name | Feature Types | Source |
---|---|---|
Road | Polyline | National Geographic Information Institute |
Bridge | ||
Building | Polygon | Ministry of Land, Infrastructure, and Transport |
Shelter | Point | Ministry of the Interior and Safety |
Population | Text | Statistics Korea |
#package networkx, pandas, geopandas, json, numpy #Input data Input: building ← Buildings, shelter ← Shelters, network ← Road networks #Output data Output: routes ← Evacuation route each building shelter_pop ← The number of people assigned to each shelter. #Calculate network geometry FOR EACH edge IN network DO edge.length = distance(edge[a], edge[b]) ← Distance between road nodes END FOR #Shortest route extraction and allocation FOR EACH building IN buildings DO FOR EACH shelter IN shelters DO route ← Dijkstra algorithm route append route to routes append pop to shelter_pop END FOR END FOR |
#Convert GeoJSON and load GDF.to_file(‘FileName.json’, driver = ‘GeoJSON’) GeoJSON = json.load(open(‘FileName.json’)) #Basemap settings Center = list(reversed(list(GDF[‘geometry’].centroid [0].coords [0]))) baseMap = folium.Map(location = center, tiles = None, overlay = False, zoom_start = n) #Create a layer group and add layer group = folium.FeatureGroup(name = ‘uljin’, overlay = False).add_to(baseMap) building = plugins.FeatureGroupSubGroup(group, name = ‘building Layer’, show =False).add_to(baseMap) population = plugins.FeatureGroupSubGroup(group, name = ‘evacuation Layer’, show = False).add_to(baseMap) #Set and apply object style style1 = {‘color’:’black’, ‘fillColor’:’#B22222, ‘weight’:0.5, ‘fillOpacity’:1.0} style2 = {‘color’:’red’, ‘weight’:1, ‘opacity’:0.5} area = folium.GeoJson(building, style_function = style1, show = False).add_to(baseMap) #Create a marker marker = folium.Marker(location = [lat, lon], popup = ‘marker’, icon = g.Icon(color = “red”)).add_to(baseMap) #Pop-up window settings popup = (‘<div style = “font-size: 15pt” > ‘ + “population:{pop}” + ’</div > ‘).format(pop = feature[‘properties’][‘POP’]) |
Possible Routes | Distance (km) | Estimated Travel Time (Minutes) | ||||
---|---|---|---|---|---|---|
Max. | Min. | Ave. | Max. | Min. | Ave. | |
29 | 7.63 | 3.39 | 4.18 | 7.63 | 3.39 | 4.18 |
Possible Routes | Distance (km) | Estimated Travel Time (Minutes) | ||||
---|---|---|---|---|---|---|
Max. | Min. | Ave. | Max. | Min. | Ave. | |
7 | 3.74 | 3.59 | 3.69 | 3.74 | 3.59 | 3.69 |
Shelter ID | Shelter Name | Capacity | Allotted Evacuees | Allotted Buildings |
---|---|---|---|---|
1 | Gocheon 1-ri Senior Center | 163 | 163 | 80 |
2 | Gocheon 1-ri Village Hall | 133 | 0 | 0 |
3 | Galjeon 2-ri Village Hall | 145 | 60 | 19 |
4 | Galjeon 1-ri Village Hall | 126 | 0 | 0 |
5 | Maryeong 1-ri Village Hall | 161 | 161 | 81 |
6 | Sugok-ri Village Hall | 98 | 98 | 20 |
7 | Sawol-ri Village Hall | 106 | 2 | 2 |
8 | Mangcheon 2-ri Village Hall | 114 | 8 | 7 |
9 | Imha 1-ri Office | 119 | 0 | 0 |
10 | Chuwol Village Hall | 81 | 0 | 0 |
# | Total | 492 | 209 |
Possible Routes | Distance (km) | Estimated Travel Time (Minutes) | ||||
---|---|---|---|---|---|---|
Max. | Min. | Ave. | Max. | Min. | Ave. | |
209 | 6.39 | 1.34 | 5.03 | 6.39 | 1.34 | 5.03 |
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Yang, B.; Kim, M.; Lee, C.; Hwang, S.; Choi, J. Developing an Automated Analytical Process for Disaster Response and Recovery in Communities Prone to Isolation. Int. J. Environ. Res. Public Health 2022, 19, 13995. https://doi.org/10.3390/ijerph192113995
Yang B, Kim M, Lee C, Hwang S, Choi J. Developing an Automated Analytical Process for Disaster Response and Recovery in Communities Prone to Isolation. International Journal of Environmental Research and Public Health. 2022; 19(21):13995. https://doi.org/10.3390/ijerph192113995
Chicago/Turabian StyleYang, Byungyun, Minjun Kim, Changkyu Lee, Suyeon Hwang, and Jinmu Choi. 2022. "Developing an Automated Analytical Process for Disaster Response and Recovery in Communities Prone to Isolation" International Journal of Environmental Research and Public Health 19, no. 21: 13995. https://doi.org/10.3390/ijerph192113995