<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM)."
Abstract:"Understanding diverse characteristics of human mobility provides profound knowledge of urban dynamics and complexity. Human movements are recorded in a variety of data sources and each describes unique mobility characteristics. Revealing similarity and difference in mobility data sources facilitates grasping comprehensive human mobility patterns. This study introduces a new method to measure similarities on two origin-destination (OD) matrices by spatially extending an image-assessment tool, the structural similarity index (SSIM). The new measurement, spatially weighted SSIM (SpSSIM), utilizes weight matrices to overcome the SSIM sensitivity issue due to the ordering of OD pairs by explicitly defining spatial adjacency. To evaluate SpSSIM, we compared performances between SSIM and SpSSIM with resampling the orders of OD pairs and conducted bootstrapping to test the statistical significance of SpSSIM. As a case study, we compared OD matrices generated from three data sources in San Diego County, CA: U.S. Census-based Longitudinal Employer-Household Dynamics Origin-Destination employment statistics, Twitter, and Instagram. The case study demonstrated that SpSSIM was able to capture similarities of mobility patterns between datasets that varied by distance. Some regions showed local dissimilarity while the global index indicated they were similar. The results enhance the understanding of complex mobility patterns from various datasets, including social media.."
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Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

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Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
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Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM)
Understanding diverse characteristics of human mobility provides profound knowledge of urban dynamics and complexity. Human movements are recorded in a variety of data sources and each describes unique mobility characteristics. Revealing similarity and difference in mobility data sources facilitates grasping comprehensive human mobility patterns. This study introduces a new method to measure similarities on two origin-destination (OD) matrices by spatially extending an image-assessment tool, the structural similarity index (SSIM). The new measurement, spatially weighted SSIM (SpSSIM), utilizes weight matrices to overcome the SSIM sensitivity issue due to the ordering of OD pairs by explicitly defining spatial adjacency. To evaluate SpSSIM, we compared performances between SSIM and SpSSIM with resampling the orders of OD pairs and conducted bootstrapping to test the statistical significance of SpSSIM. As a case study, we compared OD matrices generated from three data sources in San Diego County, CA: U.S. Census-based Longitudinal Employer-Household Dynamics Origin-Destination employment statistics, Twitter, and Instagram. The case study demonstrated that SpSSIM was able to capture similarities of mobility patterns between datasets that varied by distance. Some regions showed local dissimilarity while the global index indicated they were similar. The results enhance the understanding of complex mobility patterns from various datasets, including social media.