Rapid estimation of an earthquake impact area using a spatial logistic growth model based on social media data

Figures & data

Figure 1. The logistic function curve.

Figure 2. Schematic diagram of each variable: (a) data distribution simulation; and (b) corresponding statistics.

Figure 3. Spatial logistic growth of citizen-sensor data: the growth patterns of NP (the amount of normalized data) and MP (the accumulation of normalized data) are functions of the distance from the earthquake epicenter (r).

Table 1. The symbols used in the construction of the SLGM.

Symbols	Description
MP	Accumulation of normalized data
R	Distance from the earthquake epicenter
rm	Impact area epicentral distance indicator
K	Total amount of normalized data (carrying capacity)
l0	Maximum spatial growth rate
E	Natural logarithm base (also known as Euler's number)

Figure 4. Impact area estimation workflow.

Figure 5. Overview of the study areas and data distributions.

Table 2. Classification experiments performed using the free dataset as a training set. (Accuracy: a global score referring to the percentage of data correctly classified. Precision: the ratio of the number of correctly classified data over the total number of data classified as belonging to a particular class. Recall: the ratio of the number of correctly classified tweets over the total number of tweets belonging to a particular class in the test sets. F-score: the harmonic mean of precision and recall).

		Relevant			Not relevant
Dataset	Accuracy	Precision	Recall	F-score	Precision	Recall	F-score
L’Aquila	0.845	0.831	0.987	0.902	0.933	0.475	0.629
Emilia	0.838	0.862	0.958	0.908	0.511	0.211	0.309

Table 3. Results of the classification of our tweets and Weibo messages.

2015 Nepal earthquake		2017 Jiuzhaigou earthquake
Tweets	Type	Weibo messages	Type
Felt my floor and chair shaking. Earthquake in Delhi?	relevant	感觉刚才地震了呀 http://t.cn/Rt5qDX3 (I felt the earthquake just now.)	relevant
just felt earthquake for almost 5–10 s …	relevant	朋友圈地震了。我在这里很安全哈哈哈 http://t.cn/R2W6G4R (Know that an earthquake is happening from WeChat Moments. I'm safe here, hahaha)	not relevant
Only the gift by god is earthquake. #	relevant	抄经为地震地区祈福 (Pray for the people in the earthquake-stricken areas.)	relevant
Oh god EARTHQUAKE. More than 30sec.	relevant	是在地震了吗？怎么这么摇，我还以为是我头晕了。(Earthquake? I was dizzy from the shake.)	relevant
Still remember conversation with colleagues from @bbcnepali. Who said Kathmandu valley was prone to earthquake and destruction	not relevant	妈的，躺在床上地震了，吓死我了，一个鲤鱼打挺，但是又没有了，不敢睡了(Damn, I was lying in bed when the earthquake happened just now, I jumped out of bed and was scared to death. Dare not sleep)	relevant
My goodness earthquake, every thing shaked for a bit … Have u realized that …	relevant	我滴妈呀！吓死了！吓死了！感觉超级强烈的 …  … 地震 (My god! Scared to death! Scared to death! Feel super strong … earthquake)	relevant
…	…	…	…

Figure 6. Earthquake impact area assessment for the 2015 Nepal earthquake as derived from the SLGM analysis: (a) the model fitting result; (b) the estimated impact area.

Figure 7. Earthquake impact area assessment for the 2017 Jiuzhaigou earthquake as derived from the SLGM analysis: (a) the model fitting result; (b) the estimated impact area.

Figure 8. Comparison of the official intensity map produced by the USGS with the detection result of the impact area derived from the SLGM analysis for the 2015 Nepal earthquake.

Figure 9. Comparison of the official intensity map produced by the USGS with the detection result of the impact area derived from the SLGM analysis for the Jiuzhaigou earthquake.

Table 4. The effect of time threshold filters on the impact area detection result produced by the SLGM.

Time period (min.)	2015 Nepal earthquake			2017 Jiuzhaigou earthquake
	R^2	rm (km)	Z-measure	R^2	rm (km)	Z-measure
0–4	0.422	22	50.49%	0.919	172	51.95%
0–5	0.907	152	61.88%	0.964	149	52.61%
0–6	0.883	154	61.91%	0.960	166	52.10%
0–7	0.925	222	68.08%	0.987	182	51.75%
0–8	0.951	263	71.32%	0.976	113	54.54%
0–9	0.932	286	71.20%	0.854	17	62.46%
0–10	0.959	293	71.74%	0.871	32	93.41%
0–11	0.941	318	70.31%	0.876	29	86.27%
0–12	0.963	−1960	–	0.885	37	90.57%
0–13	0.962	−2049	–	0.832	−19	–
0–14	0.959	−2112	–	0.686	−33	–
0–16	0.956	−2130	–	0.724	−58	–
0–20	−0.24	–	–	0.774	−55	–

Table 5. SLGM detection results for additional earthquake events. (Event ID: each reported earthquake event is given a unique ID by the USGS).

Event ID	Magnitude	Depth (km)	Responses	R^2	rm (km)	Z-measure
uw10530748	6.8	51.8	26568	0.946	125	0.872
ci14151344	5.2	13.1	29319	0.979	32	0.835
ld2002042000	5.3	4.8	9950	0.988	53	0.833
nc40204628	5.5	9.7	64070	0.989	55	0.757
nm606657	5.2	14.3	42194	0.995	117	0.751
nc71863625	5.3	8.6	10114	0.996	23	0.734
se609212	5.8	6	144178	0.987	174	0.711
usp000jadn	5.7	5.2	65329	0.994	121	0.710
us10009757	5.8	12.2	15718	0.988	74	0.707
nc21323712	6.5	8.4	21984	0.906	37	0.666
nc72282711	6	11.1	41364	0.986	50	0.663
nc71996906	5.7	8	11254	0.97	44	0.656
nc30228270	6	8.1	14406	0.867	17	0.628
ci10736069	5.4	12.3	24940	0.951	54	0.621
us10006jxs	5.8	5.6	60407	0.998	157	0.611
ci14745580	5.7	8.8	38686	0.99	110	0.560
ci14383980	5.4	15.5	41176	0.996	103	0.549
ci15481673	5.1	5.1	16155	0.993	73	0.524

Figure 10. Word cloud of the most frequent terms during the two earthquakes.