Launched on 2 June 2018, Luojia-1 satellite records night-time light imagery at 130 m resolution, which is higher than most of the existing night-time light images to date. This study evaluated radiometric and spatial properties of the Luojia-1 satellite imagery for cities of Los Angeles, Wuhan and Rome as well as the change detection capability for Zunyi city. For the radiometric property, the analysis shows that the Luojia-1 images correlate well with the radiance of the Visible Infrared Imaging Radiometer Suite (VIIRS)’s Day and Night Band (DNB), and that the Luojia-1 images have a wider range of radiance values, as well as higher radiance values (e.g., 40%–90% higher) than the VIIRS DNB images. Using wavelet decomposition and change detection analysis to evaluate spatial property and change detection capability, it was found that the Luojia-1 images provide abundant spatial detail information, with about 20%–54% energy of wavelet component of the images stored in 100–400 m resolutions, and they can help to track the electrification of new roads and buildings at a fine resolution. This study shows that the Luojia-1 images are an effective data source for analysing spatiotemporal distribution of night-time light and its associated socioeconomic attributes.

In this study, we analyzed the night-time light dynamics in Iraq over the period 2012–2017 by using Visible Infrared Imaging Radiometer Suite (VIIRS) monthly composites. The data quality of VIIRS images was improved by repairing the missing data, and the Night-time Light Ratio Indices (NLRIs), derived from urban extent map and night-time light images, were calculated for different provinces and cities. We found that when the Islamic State of Iraq and Syria (ISIS) attacked or occupied a region, the region lost its light rapidly, with the provinces of Al-Anbar, At-Ta’min, Ninawa, and Sala Ad-din losing 63%, 73%, 88%, and 56%, of their night-time light, respectively, between December 2013 and December 2014. Moreover, the light returned after the Iraqi Security Forces (ISF) recaptured the region. In addition, we also found that the night-time light in the Kurdish Autonomous Region showed a steady decline after 2014, with the Arbil, Dihok, and As-Sulaymaniyah provinces losing 47%, 18%, and 31% of their night-time light between December 2013 and December 2016 as a result of the economic crisis in the region. The night-time light in Southern Iraq, the region controlled by Iraqi central government, has grown continuously; for example, the night-time light in Al Basrah increased by 75% between December 2013 and December 2017. Regions formerly controlled by ISIS experienced a return of night-time light during 2017 as the ISF retook almost all this territory in 2017. This indicates that as reconstruction began, electricity was re-supplied in these regions. Our analysis shows the night-time light in Iraq is directly linked to the socioeconomic dynamics of Iraq, and demonstrates that the VIIRS monthly night-time light images are an effective data source for tracking humanitarian disasters in that country.

Urbanization is a set of interrelated processes; the most visible among them are changes in the built-up environment. We relate those changes to human activity as expressed by online social media messages. This approach might shed light on urban dynamics currently intractable through existing datasets and methodologies. Microwave remote sensing images are used to identify urban built-up areas and changes within those areas in an objective way, while geocoded mobile social media messages deliver valuable information about human activity and the vitality found in those areas. A time-series stack of 36 TerraSAR-X Stripmap images and roughly six million social media messages were processed, classified, and visually and quantitatively analyzed for an experiment in Shanghai. We derived four possible cases of land classification by combining the results of both sources to a single raster layer at a 400m cell size. Quantifying these cases in a 2-by-2 confusion matrix shows positive and negative matches between built-up areas and social media messages. We see that correlation of positive matches is 72%. A combination of remotely sensed and social media data is a step towards a more granular analysis of urbanization processes than is possible from either data source alone. We put people in the picture of traditional remote sensing analysis

Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.

Spatial prediction of any geographic phenomenon can be an intractable problem. Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources. We present an innovative approach that combines data in a Discrete Global Grid System (DGGS) and uses machine learning for analysis. A DGGS provides a structured input for multiple types of spatial data, consistent over multiple scales. This data framework facilitates the training of an Artificial Neural Network (ANN) to map and predict a phenomenon. Spatial lag regression models (SLRM) are used to evaluate and rank the outputs of the ANN. In our case study, we predict hate crimes in the USA. Hate crimes get attention from mass media and the scientific community, but data on such events is sparse. We trained the ANN with data ingested in the DGGS based on a 50% sample of hate crimes as identified by the Southern Poverty Law Center (SPLC). Our spatial prediction is up to 78% accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%. The derived risk maps are a guide to action for policy makers and law enforcement.