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Scholars have long confirmed the agglomerative effect in retail geography. The colocation of multiple shops would encourage customers to make multiple stops in a single shopping trip. Does the aggl...

This study identified the correlation between OCO-2 XCO2 signatures and night-time light (NTL) dynamics for four different landscapes in India: Megapolis (part of Mumbai), City (part of Raipur), Town (part of Dindigul), and Village (part of Balnoi). The data used for building the correlation were collected from Orbiting Carbon Observatory-2 (OCO-2) XCO2 and Defense Meteorological Satellite Program Operational Line Scanner’s (DMSP/OLS) NTL datasets. The result of the study indicated that CO2 concentration is strongly related to NTL data. Megapolis part was found to exhibit higher values in both CO2 mean concentration (405.8 ppm) and NTL distribution level (41.5-pixel value), while the village part was low in both CO2 mean concentration (400.306 ppm) and NTL distribution level (4.4-pixel values). The city part of the data showed that the highest mean local R2 (0.964) in Geographically Weighted Regression (GWR), while the village part recorded the lowest mean local R2 (0.579) in the given data set. This study further emphasizes that the night light satellite imagery can be used as descriptors and proxies to calculate CO2 emission.

PurposeThis study uses machine machine learning techniques to assess industrial development in Africa.Design/methodology/approachThis study uses nightlight time data and machine learning techniques to assess industrial development in Africa.FindingsThis study provides evidence on how machine learning techniques and nightlight data can be used to assess economic development in places where subnational data are missing or not precise. Taken together, the research confirms four groups of important determinants of industrial growth: natural resources, agriculture growth, institutions and manufacturing imports. Our findings indicate that Africa should follow a more multisector approach for development, putting natural resources and agriculture productivity growth at the forefront.Originality/valueStudies on the use of machine learning (with insights from nightlight satellite images) to assess industrial development in Africa are sparse.

One of the challenges in globally consistent assessments of physical climate risks is the fact that asset exposure data are either unavailable or restricted to single countries or regions. We introduce a global high-resolution asset exposure dataset responding to this challenge. The data are produced using “lit population” (LitPop), a globally consistent methodology to disaggregate asset value data proportional to a combination of nightlight intensity and geographical population data. By combining nightlight and population data, unwanted artefacts such as blooming, saturation, and lack of detail are mitigated. Thus, the combination of both data types improves the spatial distribution of macroeconomic indicators. Due to the lack of reported subnational asset data, the disaggregation methodology cannot be validated for asset values. Therefore, we compare disaggregated gross domestic product (GDP) per subnational administrative region to reported gross regional product (GRP) values for evaluation. The comparison for 14 industrialized and newly industrialized countries shows that the disaggregation skill for GDP using nightlights or population data alone is not as high as using a combination of both data types. The advantages of LitPop are global consistency, scalability, openness, replicability, and low entry threshold. The open-source LitPop methodology and the publicly available asset exposure data offer value for manifold use cases, including globally consistent economic disaster risk assessments and climate change adaptation studies, especially for larger regions, yet at considerably high resolution. The code is published on GitHub as part of the open-source software CLIMADA (CLIMate ADAptation) and archived in the ETH Data Archive with the link https://doi.org/10.5905/ethz-1007-226 (Bresch et al., 2019b). The resulting asset exposure dataset for 224 countries is archived in the ETH Research Repository with the link https://doi.org/10.3929/ethz-b-000331316 (Eberenz et al., 2019)., Earth System Science Data, 12 (2), ISSN:1866-3516, ISSN:1866-3508

We propose a regional inequality-based mechanism to explain the heterogeneity in the spread of Covid-19 and test it using data from India. We argue that an area characterized by coreperiphery economic structure creates regional inequality in which the periphery remains dependent on the core for the supply of jobs, goods and services. Hence, areas arranged in coreperiphery structure induce greater degree of mobility which in turn ends up at a higher infection rate than the more homogeneously developed areas at the time of pandemic. Using nightlights data to measure regional inequality in the degree of economic activity, we find evidence in support of our hypothesis. Further, we find that regions with higher nightlight inequality also experience higher spread of Covid-19 only when lockdown measures have been relaxed and movement of goods and services are near normal. Using mobility data, we provide direct evidence in support of our proposed mechanism; that the positive relationship between regional inequality and Covid-19 infection is driven by mobility. Our findings imply that policy responses to contain Covid-19 contagion needs to be heterogeneous across India where the priority areas can be chosen ex-ante based on inequality in economic activity.

Protected area (PA), usually built to address the potential ecological pressure from different sources, aims to sustain, protect, and maintain the wilderness of nature for ecological, economic, and scenic purposes. Establishing, maintaining, and expanding the monitor network of PAs helps to enhance the ecological and biological value of these areas, especially in the light of species decline and habitat degradation. Intensively monitored and regulated through various administrative agencies, California keeps one of the best maintained network of PAs in the world, which supports various studies to address the significant threats to the preservation of wilderness from the projected warming climate and pressure from human development, including water deficit and prolonged drought, abnormality in wildfires, urban sprawl, and light pollution. With the help of satellite remote sensing technologies and geospatial analysis, we can overcome the limitation of data availability in traditional ecological studies and expand the study of PAs to a continuous gradient both in time and space. This dissertation aims to develop a comprehensive understanding through remote sensing technologies on three significant and linked ecological disturbance in California PAs, including the change of land cover, the dynamic of wildfire, and the extent and intensity of human activity reflected by stable nighttime light. Exploiting various of remote sensing observation and its derivatives, this study investigated the three topics through trend, seasonality, abnormality, spatial distribution, and hotspot of change to provide a cost-effective and repeatable studying paradigm for PA managers to better understand the past and present situation, as well as the ecological challenges of PAs. In addition, it also supplemented the study of PAs as a case study focusing on a non-tropic ecosystem, which usually involves greater interaction with human thus faces ecological pressure at a higher level.The analysis on land cover dynamics and change from 2000 to 2015 found that many protected areas in California have experienced an increase of brownness since 2000. PAs with a higher coverage of greenness are associated with a higher temporal variability, mostly because of the more complex life cycle of green vegetation and the sensitivity to the disturbance, possibly due to a drying climate regime. Partly linked to the gradual influence from the drying climate, the change in the regime of wildfire may cause a more drastic change in land cover, through the removal of aboveground biomass and its influence on the post-wildfire regrowth. In the study focusing on California PAs from 2000 to 2013, a shift in wildfire-prone land cover and season was observed starting from 2008, while the wildfires southern California shrubland during the fall season became nearly extinct and the northern California evergreen forest wildfires in the late spring increased, partly explained by the recent loss of available fuel in biomass and the increase of potential of ignition in evergreen forest as a result of prolonged drought. Most burned areas experienced a significant weakening of the immediate growing season, although the influence from fire on local landscape decayed over time, which became irrelevant after five years on average. The lower fuel moisture and a more intensive removal of aboveground biomass makes shrubs more sensitive in time, with a shorter period for biomass removal by fire and a greater delay in the start and peak of post-fire growing season.The long-enduring and sometimes intensive disturbance from human activity, especially from the development can be even more disastrous than wildfire in damaging the aboveground biomass by interrupting the natural nutrient and mass circulation,thus brings more fundamental and sometimes irreversible consequences to ecosystems. However, after decades of protection, especially the removal of settlements in side PAs, land management strategies may have bored beneficial fruits to reduce the negative effects from historical human disturbance. As a good proxy of human activity and development, the historical records of stable nighttime light from 1992 to 2012 tracked the temporal change and spatial migration of hotspot of human activity inside California PAs. A decrease of lit area at night inside PAs occurred from 1992 to 2012, with a turning point around 2004 when the relatively sharp decrease started from became more gradual thereafter. Besides, area covered by higher stable nighttime light not only shrank, but also retreated from the area with high wilderness. If solidly confirmed by other socioeconomic variable, the above finding demonstrated the effectiveness of PA establishment and the fruits of multiple conservation strategies kept for decades. The investigation of three major disturbances to the wilderness of California PAs provided an example of exploiting the cost-effective and continuously available observation of the Earth surface at a regional level to understand the ecological challenges of PAs. The findings can help the National Park Service and other related agencies of PA administration in the review, adjustment, and proposal of regulations and policies regarding to PAs. Future work and analysis can be focused on improving the accuracy of predictive models involved the analysis, either by concentrating on the difference between categories of protection, or through a better understanding on the related ecological process and mechanisms to identify or develop better explanatory variables. In addition, when conducting analysis on PAs, the buffer area adjacent to PAs cannot be ignored, which is an important source of disturbance to PAs as well as an ecological corridor that links nearby PAs into clusters. Considering the great diversity in the landscape of PAs, the analytical results can be aggregated and interpreted by different ecoregions to derive more accessible guidance to local policy makers.

Satellite data and randomized controlled trials (RCTs) are a powerful combination for analyzing causal effects beyond traditional survey-based indicators. The usage of remotely collected data for evaluating RCTs is cost-effective, objective and possible for anyone with treatment assignment data. By re-evaluating one of the largest RCTs - the smartcard intervention of Muralidharan et al. (2016) covering 20 million people - with Indian nighttime luminosity, this paper finds that nightlights as a specific type of satellite data likely often are too noisy to evaluate RCTs. Building upon a post-treatment and a Difference-in-Differences approach, we do not find any statistically significant effects of the biometric smartcards on nightlights, contrasting Muralidharan et al. (2017)'s results of higher income level in treated areas. This can be mainly explained either with the noisiness-caused inability of nightlights to specifically capture economic effects or the absence of an increased economic activity due to a simple redistributive effect of the intervention. The former is more likely when looking at GDP implications of the noisiness in the luminosity data. Per head estimates, sensitivity checks for spillovers, subdistrict-level instead of village-level observations and different time-wise aggregations of nightlight data do not lead to changed results. Although limited with nightlights, nonetheless, the potential for re-evaluating RCTs with satellite data in general is enormous in three ways: (1) For confirming claimed treatment effects, (2) to understand additional impacts and (3) for cost-effectively understanding long-term impacts of interventions. Using daytime imagery for analyzing RCTs is a promising direction for future research.

In recent decades, hurricane frequency and intensity have increased in the Caribbean Basin. From 2000 to 2012, more than 100 hurricanes impacted lives, infrastructure, and economic activity along the region’s shorelines. Studies suggest that mangrove forests’ dense root systems might mitigate the impact of hurricanes, which would help stabilize the coastline and prevent erosion from waves and storms. Although many tropical mangroves are found on Caribbean coasts, climatic and anthropogenic events have been clearing these wetland ecosystems at an annual rate of 1 percent since the 1990s. This study quantifies the effects of hurricane windstorms on economic activity using nightlight as a proxy at the highest spatial resolution data available (1 square kilometer). Using different widths of the mangrove belt, it measures levels of mangrove natural protection against the impact of hurricanes and studies the broader socioeconomic and environmental effects of this protection. The results suggest that while major hurricanes reduce nightlight by approximately 2 percent and up to 16 percent in storm surge prone areas, the presence of mangroves on the coast mitigates the impact of hurricanes, reducing nightlight by 1–6 percent.

One recent trend in optical remote sensing is to increase observation frequencies. However, there are still challenges on the night side when sunlight is not available. Due to their powerful capabilities in low-light sensing, nightlight satellite sensors have been deployed to capture nightscapes of Earth from space, observing anthropomorphic and natural activities at night. To date, the mainstream of nightlight remote sensing applications has mainly focused on artificial lights, especially within cities or self-luminous bodies, such as fisheries, oil, offshore rigs, etc. Observations taken under moonlight are often discarded or corrected to reduce lunar effects. Some researchers have discussed the possibility of using moonlight as a useful illuminating source at night for the detection of nocturnal features on Earth, but no quantitative analysis has been reported so far. This study aims to systematically evaluate the potential of moonlight remote sensing with mono-spectral Visible Infrared Imaging Radiometer Suite/Day-Night-Band (VIIRS/DNB) imagery and multi-spectral photos taken by astronauts from the International Space Station (ISS), as well as unmanned aerial vehicle (UAV) night-time imagery. Using the VIIRS/DNB, ISS and UAV moonlight images, the possibilities of the moonlight remote sensing were first discussed. Then, the VIIRS/DNB, ISS, UAV images were classified over different non-self-lighting land surfaces to explore the potential of moonlight remote sensing. The overall accuracies (OA) and kappa coefficients are 79.80% and 0.45, 87.16% and 0.77, 91.49% and 0.85, respectively, indicating a capability to characterize land surface that is very similar to daytime remote sensing. Finally, the characteristics of current moonlight remote sensing are discussed in terms of bands, spatial resolutions, and sensors. The results confirm that moonlight remote sensing has huge potential for Earth observation, which will be of great importance to significantly increase the temporal coverage of optical remote sensing during the whole diurnal cycle. Based on these discussions, we further examined requirements for next-generation nightlight remote sensing satellite sensors.

Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, a...

Impervious surface detection is significant to urban dynamic monitoring and environment management. One of the most effective approaches to evaluating the impervious surface is the use of nighttime light imagery. However, little work on this subject was carried out with the new generation nighttime light data from Luojia 1-01 satellite, which has a finer spatial resolution than the predecessors such as the nightlight data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite. Therefore, this study conducted the first investigation of the capacity of Luojia 1-01 nighttime light data in detecting the extent and degree of impervious surfaces. Focusing on three cities of Beijing, Shanghai, and Guangzhou, several maps of the spatial extent of impervious surface areas were first extracted from two types of nighttime lights data (Luojia 1-01 and NPP-VIIRS data) by applying a dynamic threshold segmentation method. Meanwhile, a series of polynomial regression models were adopted to estimate the relation between imperiousness degree and light intensity. The results compared with the reference data derived from Landsat 8 Operational Land Imager (OLI) show that Luojia 1-01 data can produce a more precise map of the spatial extent of impervious surfaces than NPP-VIIRS data owing to the finer spatial resolution and the wider measurement range. Nevertheless, Luojia 1-01 data failed to provide reliable estimates of the imperviousness degree in comparison with NPP-VIIRS data as this nighttime light imagery with finer spatial resolution can better discriminate the surfaces that have the same imperviousness degree but are illuminated with different light intensities, consequently resulting in a weak correlation between imperviousness degree and light intensity. The over- and under-estimates of imperviousness degree suggested an increase in spatial resolution of nightlight imagery does not always improve the accuracy and reliability of nighttime light-based estimations. These study results confirmed that Luojia 1-01 nightlight imagery is a potential and promising data source for mapping the spatial extent of impervious surface areas, but difficult to accurately estimate the imperviousness degree. Future research may improve the accuracy of imperviousness degree estimation by integrating the Luojia 1-01 nightlight imagery with other useful data sources.

The Dominican Republic is highly exposed to adverse natural events putting the country at risk of losing hard-won economic, social, and environmental gains due to the impacts of disasters. This study uses monthly nightlight composites in conjunction with a wind field model to econometrically estimate the impact of tropical cyclones on local economic activity in the Dominican Republic since 1992. It is found that the negative impact of storms lasts up to 15 months after the strike, with the largest effect observed after nine months. Translating the reduction in nightlight intensity into monetary losses by relating it to quarterly gross domestic product suggests that on average the storms reduced gross domestic product by about US$1.1 billion (4.5 percent of gross domestic product in 2000 and 1.5 percent in 2016).

The object of research is the process of sustainable development of territorial units on the example of the regions of Ukraine. The concept of sustainable development has become a leading development strategy for most countries in the world. One of the biggest challenges is the availability of complete and verified data for sustainable development assessment models. The method used to calculate the index of sustainable development, developed in the World Data Center for Geoinformatics and Sustainable Development of the National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute». This methodology is based on calculating the metric of the index of sustainable development based on measurements of the quality of life of the population and the components of the safety of living for individual countries and regions. For the application of the methodology at the regional level, it was suggested to use information on the night lighting of the territory received by means of remote sensing of the Earth from satellites. The character and cohesiveness of the connection between the brightness of night lighting and indicators of sustainable development are studied. It is established that the most significant link exists between indicators of the economic development index, the index of influence on climate change and night lighting of the regions of Ukraine. Based on geographic information analysis of ArcGIS software, ESRI has applied statistical zoning tools that provide opportunities for statistical processing of satellite images within the regionally or regionally allocated areas. On the basis of the mathematical apparatus of the intellectual data analysis, a global and local regression analysis of the relationships between the revealed indicators was carried out. Consideration of the cramped nature of this connection in the territorial section revealed areas with varying degrees of close ties, which is explained by the peculiarities of the socio-economic development of the territories. Due to this, it is possible to obtain the calculation of similar indicators at more detailed territorial levels, corresponding to separate regions or cities of regional significance.

DMSP-OLS stаble nighttime light dаta version 4 (1 km resolution) from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) temporal nighttime light dataset provides a new way of information to monitor human activities on a global scale. However, the DMSP-OLS sensor data cannot be used directly to compare with other satellite datasets for human activities because it has no onboard calibration mechanism. This study used a newly developed systematically correcting temporal multi-satellite nightlight data from 1992 to 2013 for Kyrgyzstan, validation with different data sources (Landsat, Global Human Settlements, and population datasets). The results revealed the effectiveness of the proposed validation and data correction method for urban growth dynamics with reducing errors, signal distortion, and discrepancies from the nightlight dataset and improved the quality and comparability of data with other datasets. The urban expansion dynamics are computed for Kyrgyzstan with average accuracy and kappa statistics of about 0.80% and 0.61%. Furthermore, the results showed that the stable nightlight dataset provides valuable information for monitoring urban expansion and its impacts on land cover dynamics. The study provides a useful preliminary information tool for urban planners and policy and decision-makers for the better management of urban planning in the main cities of Kyrgyzstan in the context of the day-by-day increasing population trend.

The authors wish to make the following corrections to this paper [...]

Nighttime light (NTL) emissions are widely used across disciplines to map the spatial distribution of a variety of socioeconomic variables. For economic studies, NTLs allow to proxy for levels of economic indicators at the country level as well as the local level. Further, multi-temporal differences in NTL intensity are also related to GDP differences on the country level. In this study, we investigate if this relation in temporal differences also holds for the local level. We test this with DMSP as well as VIIRS NTLs data from 2010–2015 in Nigeria, Tanzania, and Uganda. Even though we successfully map local levels of socio-economic status with NTLs, we find multi-temporal changes in NTLs at this local level to be uncorrelated with socio-economic development over time. We conclude that luminosity values based on current DMSP and VIIRS sensors are no silver bullet in measuring local economic changes over time and should, if at all, only be used with caution for this.

The goal of this project is to investigate global nightlights and light pollution as indicators for the economic growth and urbanization of a nation. We test this proposition using the nightlight data obtained by the Defense Meteorological Satellite Program (DMSP) of the National Oceanic and Atmospheric Administration (NOAA). Through examining the changes over time of spatial nightlight patterns for different developing countries on Google Earth Engine (GEE), we investigate how they correlate to countries' development indicators such as Gross Domestic Product (GDP) and other parameters which represent new reference indicators with respect to previous research in the literature. A preliminary analysis brings to interesting results and discussion.

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.

Racing bib number (RBN) detection and recognition is a specific problem related to text recognition in natural scenes. In this paper, we present a novel dataset created after registering participants in a real ultrarunning competition which comprises a wide range of acquisition conditions in five different recording points, including nightlight and daylight. The dataset contains more than 3K samples of over 400 different individuals. The aim is to provide an “in the wild” benchmark for both RBN detection and recognition problems. To illustrate the present difficulties, the dataset is evaluated for RBN detection using different Faster R-CNN specific detection models, filtering its output with heuristics based on body detection to improve the overall detection performance. Initial results are promising, but there is still significant room for improvement. And detection is just the first step to accomplish “in the wild” RBN recognition.

This article presents a system developed for the collection and analysis of traffic data obtained from traffic camera videos using computational intelligence. The proposed system is developed using the modern object detection library Detectron2. A pipeline-type architecture is used for frame processing, where each step is an independent, configurable functional module, loosely coupled to the others. The validation of the proposed system is performed on real scenarios in Montevideo, Uruguay, under different conditions (daylight, nightlight, and different video qualities). Results demonstrate the effectiveness of the system in the considered scenarios.

The accelerating climatic changes and new infrastructure development across the Arctic require more robust risk and environmental assessment, but thus far there is no consistent record of human impact. We provide a first panarctic satellite-based record of expanding infrastructure and anthropogenic impacts along all permafrost affected coasts (100 km buffer, ≈6.2 Mio km2), named the Sentinel-1/2 derived Arctic Coastal Human Impact (SACHI) dataset. The completeness and thematic content goes beyond traditional satellite based approaches as well as other publicly accessible data sources. Three classes are considered: linear transport infrastructure (roads and railways), buildings, and other impacted area. C-band synthetic aperture radar and multi-spectral information (2016–2020) is exploited within a machine learning framework (gradient boosting machines and deep learning) and combined for retrieval with 10 m nominal resolution. In total, an area of 1243 km2 constitutes human-built infrastructure as of 2016–2020. Depending on region, SACHI contains 8%–48% more information (human presence) than in OpenStreetMap. 221 (78%) more settlements are identified than in a recently published dataset for this region. 47% is not covered in a global night-time light dataset from 2016. At least 15% (180 km2) correspond to new or increased detectable human impact since 2000 according to a Landsat-based normalized difference vegetation index trend comparison within the analysis extent. Most of the expanded presence occurred in Russia, but also some in Canada and US. 31% and 5% of impacted area associated predominantly with oil/gas and mining industry respectively has appeared after 2000. 55% of the identified human impacted area will be shifting to above 0 ∘C ground temperature at two meter depth by 2050 if current permafrost warming trends continue at the pace of the last two decades, highlighting the critical importance to better understand how much and where Arctic infrastructure may become threatened by permafrost thaw.

This article demonstrates the construction of earthquake and volcano damage indices using publicly available remote sensing sources and data on the physical characteristics of events. For earthquakes we use peak ground motion maps in conjunction with building type fragility curves to construct a local damage indicator. For volcanoes we employ volcanic ash data as a proxy for local damages. Both indices are then spatially aggregated by taking local economic exposure into account by assessing nightlight intensity derived from satellite images. We demonstrate the use of these indices with a case study of Indonesia, a country frequently exposed to earthquakes and volcanic eruptions. The results show that the indices capture the areas with the highest damage, and we provide overviews of the modeled aggregated damage for all provinces and districts in Indonesia for the time period 2004 to 2014. The indices were constructed using a combination of software programs—ArcGIS/Python, Matlab, and Stata. We also outline what potential freeware alternatives exist. Finally, for each index we highlight the assumptions and limitations that a potential practitioner needs to be aware of.

Photoluminescent pigments are rare earth metals that absorb light and emit it slowly as a pale blue/bluish green light over 6–8 h after initial excitation from external light source. These pigments can be incorporated in textiles as prints, but there is limited literature to guide textile and fashion designers on how these pigments may be used to create novel-illuminated patterns of luminescence as prints for home fashion. Therefore, this research aims to systematically study the properties of photoluminescent pigments and develop design concepts on textiles for home fashion using user-centric design research in a real-time setting for night-time navigation. The pilot study indicated that the photoluminescent pigment printed textiles could provide navigational aid and focus lighting in the areas of orientation lighting and edge definition. The study indicates that the textile products in home fashion may be designed that improve the light cognition for all age groups and demographics using photoluminescent pigment textiles. Thereby introducing alternate methods of light sources that promote natural nightlight so as to create a paradigm shift in the way lighting design is perceived by designers and architects.

Measuring timely high-resolution socioeconomic outcomes is critical for policy making and evaluation, but hard to reliably obtain. With the help of machine learning and cheaply available data such as social media and nightlight, it is now possible to predict such indices in fine granularity. This paper demonstrates an adaptive way to measure the time trend and spatial distribution of housing activeness with the help of multiple easily accessible datasets. We first identified the regional activeness status from energy consumption data and then matched it with nightlight and land use data. We introduce the factor-adjusted regularization methods for prediction (FarmPredict) to deal with dependence and collinearity issues among predictors by effectively lifting the prediction space. It applies to all machine learning algorithms. The heterogeneity of big data is mitigated through the land-use data. FarmPredict allows us to extend the regional results to the city level, with a 75% out-of-sample explanation of the spatial and timeliness variation in the house usage. Since energy is indispensable for life, our method is highly transferable with only the requirement of publicly accessible data. Our paper demonstrates the power of machine learning in understanding socioeconomic outcomes when the census and survey data are costly or unavailable.

This study examined the potential geolocation biases/errors in the Defense Meteorological Satellite Program (DMSP) nightlight data that has been used in carbon emission models, such as the global high-resolution emission inventory Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Quantifying and mitigating the bias has become an urgent, critical task in obtaining robust emission estimates for urban areas, the major sources of the global carbon emissions, from nightlight-based emission estimates. We first attempted to characterize the bias by changing the location of urban cores identified by the DMSP data and their orientation using the city boundary data from the Open Street Map (OMS) as a reference. We hypothesized that a geolocation bias-free emission map should maximize the total emission within the administrative boundaries, and developed an iterative optimization algorithm to obtain correction vectors (distance and angle) for shifting emission data (or DMSP data). We implemented the proposed algorithms for many global cities from different continents, and discovered the latitudinal dependence of the bias. The paper is focused on the methodological aspects of the bias correction, and the implementation and applications to global cities.

By using freely available remote sensing data, typically used for natural hazard modeling, and combining these with nightlight data as a proxy for economic activity, this paper constructs n...

This study develops an approach, algorithms and software to analyse the bias of nightlights remote sensing satellite data and corresponding bias calculated on the basis of global data on greenhouse gas emissions. We used city boundary data from Open Street Map, as well as high-resolution Open-Source Data Inventory for Anthropogenic CO2 (ODIAC) data on carbon dioxide emissions. We based the algorithms of the analysis on the iterative calculation of correction vectors for shifting emission data that provide the maximum magnitude of carbon dioxide emissions within the administrative boundaries of cities. We demonstrate the main stages of the proposed algorithm implementation for a number of cities from different continents.

BackgroundCompilation of emission inventories (EIs) for cities is a whole new challenge to assess the subnational climate mitigation effort under the Paris Climate Agreement. Some cities have started compiling EIs, often following a global community protocol. However, EIs are often difficult to systematically examine because of the ways they were compiled (data collection and emission calculation) and reported (sector definition and direct vs consumption). In addition, such EI estimates are not readily applicable to objective evaluation using modeling and observations due to the lack of spatial emission extents. City emission estimates used in the science community are often based on downscaled gridded EIs, while the accuracy of the downscaled emissions at city level is not fully assessed.ResultsThis study attempts to assess the utility of the downscaled emissions at city level. We collected EIs from 14 major global cities and compare them to the estimates from a global high-resolution fossil fuel CO2emission data product (ODIAC) commonly used in the science research community. We made necessary adjustments to the estimates to make our comparison as reasonable as possible. We found that the two methods produce very close area-wide emission estimates for Shanghai and Delhi (ConclusionsThe relatively good agreement suggests that the ODIAC data product could potentially be used as a first source for prior estimate of city-level CO2emission, which is valuable for atmosphere CO2inversion modeling and comparing with satellite CO2observations. Our compilation of in-boundary emission estimates for 14 cities contributes towards establishing an accurate inventory in-boundary global city carbon emission dataset, necessary for accountable local climate mitigation policies in the future.

An accurate modeling of urban CO2 emissions is important for understanding the dynamics of carbon cycle and for designing low-carbon policies. We develop an improved nightlight-based method to model urban CO2 emissions and investigate their spatiotemporal patterns. Differing from the previous methods, in processing the pre-modeling data, we bring forward the existing CO2 inventories from national and provincial levels to city level, and correct the saturation and blooming problems of nightlight. In modeling the correlation between nightlight and statistically accounted CO2 emissions, we highlight a panel-data regression analysis that considers the spatiotemporal heterogeneity across cities and over time simultaneously. Eleven cities in Yangtze River Delta of China were selected for a case study testing our method. The internal and external validations have proven the predominance of our proposed method for capturing the nightlight-CO2 correlation, and for describing the spatial distribution and heterogeneity of urban CO2 emissions.

The prevalence of overweight and obesity are increasing in many African countries and hence becoming regional public health challenges. We employ satellite-based night light intensity data as a proxy for urbanization to investigate the relationship between urbanization and women's body weight. We use two rounds of the Demographic and Health Survey data from Nigeria. We employ both nonparametric and parametric estimation approaches that exploit both the cross-sectional and longitudinal variations in night light intensities. Our empirical analysis reveals nonlinear relationships between night light intensity and women's body weight measures. Doubling the sample's average level of night light intensity is associated with up to a ten percentage point increase in the probability of overweight. However, despite the generally positive relationship between night light intensity and women's body weight, the strength of the relationship varies across the assorted stages of night light intensity. Early stages of night light intensity are not significantly associated with women's body weight, while higher stages of nightlight intensities are associated with higher rates of overweight and obesity. Given that night lights are strong predictors of urbanization and related economic activities, our results hint at nonlinear relationships between various stages of urbanization and women's body weight.

We propose a Bayesian factor analysis model as an alternative to the Human Development Index (HDI). Our model provides methodology which can either augment or build additional indices. In addition to addressing potential issues of the HDI, we estimate human development with three auxiliary variables capturing environmental health and sustainability, income inequality, and satellite observed nightlight. We also use our method to build a Millennium Development Goals (MDG) index as an example of constructing a more complex index. We find the “living standard” dimension provides a greater contribution to human development than the official HDI suggests, while the “longevity” dimension provides a lower proportional contribution. Our results also show considerable levels of disagreement relative to the ranks of official HDI. We report the sensitivity of our method to different specifications of spatial correlation, cardinal-to-ordinal data transforms, and data imputation procedures, along with the results of a simulated data exercise.

This paper is the first to examine the short term local economic impact of tropical cyclones by estimating the effects on monthly nightlight intensity. More specifically, for Guangdong Province in Southern China, we proxy monthly economic activity with remote sensing derived monthly night time light intensity and combine this with local measures of wind speed derived from a tropical cyclone wind field model. Our regression analysis reveals that there is only a significant (negative) impact in the month of the typhoon strike and nothing thereafter. Understanding that typhoons are inherently a short-term phenomenon has possible implications for studies using more aggregate data.

In response to global warming, a spatiotemporal inventory of city-level CO2 emissions is of great importance for the developing countries, especially for China of which CO2 emissions increase rapidly but lack in energy statistics at urban scale. Currently the nightlight imagery from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) has been widely used as a promising data source for estimating the spatiotemporal distributions of CO2 emissions. However, most previous studies ignored the saturation problem of DMSP-OLS nightlight imagery which could result in more uncertainties in CO2 emission estimations. Moreover, little work was carried out to evaluate the important emission sources from a large population living in the unlit areas with the consideration of different emission levels. To address these issues, this study proposed an improved allocating model to map the city-level energy-related CO2 emissions of mainland China in 2000, 2005, 2010, and 2013 based on an enhanced vegetation index (EVI) adjusted nighttime light index (EANTLI) and LandScan population data. Through the comparison with the original nightlight images, the EANTLI is proved to increase inter-urban variability and alleviate DMSP-OLS saturation. Meanwhile, the accuracy assessment with the statistical data of CO2 emissions at the level of city units has also demonstrated that the proposed model is appropriate and reliable in estimating CO2 emissions not only in the lit areas but also in the unlit areas. The model outputs presented that cities with huge amounts of CO2 emissions mainly agglomerated in the southern and eastern coastal regions, while spatial distributions of cities with small amounts of CO2 emissions mainly appeared in western and central regions. These results could improve the understanding of regional discrepancies of spatiotemporal CO2 emission dynamics at urban scale, and provide a scientific basis for policymaking on viable CO2 emission mitigation policies.

The spread of artificial night lighting is increasingly acknowledged as a major threat to global biodiversity. Identifying and exploring the impacts of nightlight pollution upon species behavior, ecology and population dynamics could enhance conservation capacity. Sea turtle hatchlings emerge from nest at night and use visual cues to direct towards the brightest and lowest horizon, eventually leading them to the sea. Nightlight pollution could alter the cues perceived, disorienting the fragile hatchlings. We examined the level of artificial lighting and orientation patterns of sea turtles hatchling, in Zakynthos Island, Greece, one of the main nesting rookeries of the loggerheads ( Caretta caretta ) in the Mediterranean Sea. We analyzed movement patterns of 5967 hatchlings from 230 nests, and demonstrate that nightlight pollution could reduce population recruitment by more than 7%, suggesting that mitigation measures should become a high conservation priority. Our results further suggest that the responses of sea turtle hatchlings to artificial nighttime lighting could vary significantly depending on various factors, either anthropogenic or natural. Local conditions operating at the nesting site level determine the fine scale responses of hatchlings, thus conservation measures should be drawn in respect to site-specific properties.

Daily changes in human activity are difficult to detect using nightlight imagery because many factors that influence nightlights are changing from night to night. We propose using a difference-in-differences methodology for detecting daily changes in human behavior using NASA's Black Marble product suite. We find that total top-of-atmosphere radiance on the Indonesian island of Bali decreases by close to 100% during the Hindu holiday of Nyepi, relative to counterfactual islands. We then use changes in total radiance during Nyepi to predict census information on religion in this region of Indonesia; nighttime imagery can explain 78% of the variation in religion at the equivalent of the county level, and 62% of the variation at the equivalent of the zipcode level. We use the same method to correctly identify pockets of large Hindu populations on the predominantly Muslim islands of Java and Lombok.

Governments have incentives to misreport their economic productivity to advance their political goals. These incentives have long been understood, but the validity of government data has been difficult to estimate in the absence of viable external estimates. Using historic Defense Meteorological Satellite Program's Operational Linescan System nightlights imagery we corroborate reports that Panama's government data has been increasingly politicised since the handover of the Panama Canal on 31 December 1999. The Canal Handover represents a “natural experiment” in which the production of government data changed in Panama for reasons separate from the desire to manipulate that data. The amount of light a country produces at night, known as nightlight production, has been shown to strongly correlate with GDP. Using subnational Panamanian nightlight production from 1996 to 2012, we detect a significant divergence between the relationship of subnational reported GDP and nightlights before the Canal handover (when the U.S.A. was very involved in their statistical agencies) and the correlation after the handover (with no U.S. involvement). Our results indicate that between 2000 and 2012, Panama reported approximately 19% more GDP than what was expected by their nightlight production from 2000 to 2012, or a total of around 40 billion U.S. dollars. Our results suggest governments may engage in political manipulation of government statistics to improve the appearance of government performance. While indirect data can never definitely confirm economic phenomena, this analysis presents a unique research design and application of historic satellite imagery to corroborate reports of GDP misreporting.

It is a hotpot that extraction the floor area ratio from high resolution remote sensing images. It is a development trend of using nightlight data to survey the urban social and economic information. This document aims to provide a conference relationship model for VIIRS/NPP nightlight data and floor Area Ratio from High Resolution ZY-3 Images. It shows that there is a lineal relationship between the shadow and the floor area ratio, and the R2 is 0.98. It shows that there is a quadratic polynomial relationship between the floor area ratio and the nightlight, and the R2 is 0.611. We can get a conclusion that, VIIRS/NPP nightlights data may show the floor area ratio in an extent at level of administrative street.

The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is a NASA Earth Venture Instrument set to fly in the 2018-2021 timeframe and cover North America from a geostationary orbit. TEMPO is a powerful spectrometer sampling UV and visible wavelengths in two channels (290-490 nm and 540-740 nm) in 0.2 nm steps. This spectroscopic capability will enable TEMPO to characterize many types of artificial lighting at night in addition to its regular daytime air quality and atmospheric chemistry mission. This paper describes the TEMPO instrument and our planned approach to make nighttime observations. A spectral retrieval algorithm will estimate the contributions from sources in a spectral library to map out the usage of various types of lighting (e.g., Hg vapor, high and low pressure Na vapor, LED, fluorescent, and incandescent) over North America. We calculate the sensitivities of such nightlight retrievals using the measured properties of our flight detectors and optics, and conclude that TEMPO will complement well the VIIRS Day-Night Band for the study of artificial lighting at night from space by offering a new and powerful capability to discriminate between lighting types.