Estimating Economic Activity from Space

Accurate estimates of the magnitude and spatial distribution of both formal and informal economic activity is necessary to achieve various social and economic goals of societies and countries at different levels of analysis. However, collection of data on economic variables, especially of national and sub-national income levels is problematic due to various shortcomings in the data collection process. Additionally, the informal economy estimates are often excluded from official statistics. Thus, developing alternative methods for estimating these economic activities may prove to be useful and necessary. This research demonstrates the potential of developing spatially explicit estimates of economic activity from nighttime satellite imagery as provided by the Defense Meteorological Satellite Program\u27s Opertaional Linescan System (DMSP-OLS). The methods presented here are used to estimate formal and informal economic activity of Mexico and India at the sub-national level and to create a disaggregated global map of total economic activity. Regression models were developed between spatial patterns of nighttime imagery and Adjusted Official Gross State Product (AGSP) for the U.S. states. The regression parameters derived from the regression models of the U.S. were blindly applied to Mexico to estimate the Estimated Gross State Income (EGSI) at the sub-national level and the Estimated Gross Domestic Income (EGDI) at the national level. Comparison of the EGDI estimate of Mexico and official Gross National Income (GNI) statistic demonstrated that the informal economy and inflow of remittances for Mexico was about 50 percent larger than what was recorded in the official GNI statistic. However, when the regression parameters were applied to India, Gross State Income (GSI) was underestimated for most of the states and Union Territories (UTs) of India in comparison to their official GSP, although it provided a high correlation (r = 0.93) between them. This was probably because of the lower level of urbanization in India in comparison to the U.S. To adjust for the different levels of urbanization in the U.S. and India, the EGDI was multiplied by the ratio of the percentage of the population in urban areas for the two countries. Comparing the Adjusted Estimated Gross Domestic Income (AEGDI) with the official GNI statistic of India suggested that the magnitude of India\u27s informal economy and the inflow of remittances may also be 50 percent larger than what was recorded in the official GNI value. Lastly, a global disaggregated map of total (formal plus informal) economic activity was created. This was done by multiplying the sum of light intensity values of the administrative units (states of China, India, Mexico, and the U.S., and other countries of the world) with computed unique coefficients. This provided estimated total economic activity (GSPIi and GDPIi ) for each administrative unit. The total economic activity values were spatially distributed (disaggregated) within each administrative unit using the percentage contribution of agriculture towards GDP for each country, combined with raster representations of the nighttime lights image and the LandScan population grid. This generated a spatially disaggregated 30 arc-second or one km2 map of estimated total economic activity

Assessing Income Distribution at the District Level for India Using Nighttime Satellite Imagery

Several studies have been carried out relating nighttime lights with economic activity.But most studies relating nighttime lights with economic activity have focused on associatinghigher totals in economic activity with higher sum of lights across regions. The questionaddressed in this paper is how best to model the relationship of nighttime lights with not just thewealthy but also the relatively worse-off within a region. The implications of such an exerciseare immense with respect to ascertaining income distribution aspects of any area. The methodsdeveloped in this paper explore the relation between households in different income brackets atthe district level for India, and the radiance-calibrated nighttime image of 2004. Besides theradiance-calibrated data of 2004, estimates of household incomes and number of households indifferent income brackets, made by Indicus Analytics (specialized economic research firm, basedin New Delhi, India) were used. The results were mapped and insights were drawn for alldistricts based on their socio-economic profile. These results illustrate the advantage of using thiseasily available data for determining income inequalities, especially in information-deficientcountries such as India

Development of a 2009 Stable Lights Product using DMSP-OLS data

Since 1994, NGDC has had an active program focused on global mapping of nighttime lights using the data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors.  The basic product is a global annual cloud-free composite, which averages the OLS visible band data for one satellite from the cloud-free segments of individual orbits.  Over the years, NGDC has developed automatic algorithms for screening the quality of the nighttime visible band observations to remove areas contaminated by sunlight, moonlight, and the presence of clouds.  In the Stable Lights product generation, fires and other ephemeral lights are removed based on their high brightness and short duration.  Background noise is removed by setting thresholds based on visible band values found in areas known to be free of detectable lights.  In 2010, NGDC released the version 4 time series of Stable Lights, spanning the years 1992-2009.  These are available online at <http://www.ngdc.noaa.gov/dmsp/downloadV4composites.html>

The Annual Cycling of Nighttime Lights in India

India is known to have unstable power supply, and many locations show an annual cycle in VIIRS Nighttime Light (VNL). In this study, autocorrelation function (ACF) analysis is used to identify the annual cycling in VNL. Two fundamentally different classification techniques are proposed to classify the ACF profile into one of the three arch types, i.e., acyclic, single peak, and dual peak. The results from the two classification techniques are closely compared to verify their output. This analysis is carried out for the entire territory of India in 15 arc second grid cells. The power stability data acquired from the India Human Development Survey (IHDS) and the Electricity Supply Monitoring Initiative (ESMI) are used to verify their relationship to the annual cycling of VNL. To further aide the analysis, land use/land class are accounted for by data from the India National Remote Sensing Center (NRSC). As a result, the contribution of power stability to VNL annual cycling in India is inconclusive due to the limitation of power stability data. Furthermore, other potential factors should be further examined

Magnitude Matters: The Impact of Pandemic Threat Perceptions on the Effectiveness of Health Message Framing Across Countries

Pandemic diseases are characterized as being highly contagious, where there is limited control and a threat of spreading globally. During a pandemic outbreak, hysteria and media hype make it difficult for medical authorities to get accurate and useful information to individuals to minimize the spread of the epidemic. This research investigates the impact of message framing on intentions to interact with health messages, taking into account perceived magnitude of the pandemic threat. The authors conduct research in three countries – U.S., China, and Ghana. Study 1 was a between-subjects design to examine the impact of message frame (positive, negative) in a call-to-action disease message on intentions to click for more information in Ghana, the U.S., and China. Study 2 was a 2 (message frame: positive, negative) by 3 perceived threat magnitude (high, moderate, low) between-subjects design to examine the impact of each variable on intentions to click for more information in China and the U.S. Findings show that magnitude matters in health message framing. Specifically, message framing effects are evident when the perceived magnitude of a threat is moderate

Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019

A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm2/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (\u3c0.6 nW/cm2/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses

Indicators of Electric Power Instability from Satellite Observed Nighttime Lights

Electric power services are fundamental to prosperity and economic development. Disruptions in the electricity power service can range from minutes to days. Such events are common in many developing economies, where the power generation and delivery infrastructure is often insufficient to meet demand and operational challenges. Yet, despite the large impacts, poor data availability has meant that relatively little is known about the spatial and temporal patterns of electric power reliability. Here, we explore the expressions of electric power instability recorded in temporal profiles of satellite observed surface lighting collected by the Visible Infrared Imaging Radiometer Suite (VIIRS) low light imaging day/night band (DNB). The nightly temporal profiles span from 2012 through to mid-2020 and contain more than 3000 observations, each from a total of 16 test sites from Africa, Asia, and North America. We present our findings in terms of various novel indicators. The preprocessing steps included radiometric adjustments designed to reduce variance due to the view angle and lunar illumination differences. The residual variance after the radiometric adjustments suggests the presence of a previously unidentified source of variability in the DNB observations of surface lighting. We believe that the short dwell time of the DNB pixel collections results in the vast under-sampling of the alternating current lighting flicker cycles. We tested 12 separate indices and looked for evidence of power instability. The key characteristic of lights in cities with developing electric power services is that they are quite dim, typically 5 to 10 times dimmer for the same population level as in Organization for Economic Co-operation and Development (OECD) countries. In fact, the radiances for developing cities are just slightly above the detection limit, in the range of 1 to 10 nanowatts. The clearest indicator for power loss is the percent outage. Indicators for supply adequacy include the radiance per person and the percent of population with detectable lights. The best indicator for load-shedding is annual cycling, which was found in more than half of the grid cells in two Northern India cities. Cities with frequent upward or downward radiance spikes can have anomalously high levels of variance, skew, and kurtosis. A final observation is that, barring war or catastrophic events, the year-on-year changes in lighting are quite small. Most cities are either largely stable over time, or are gradually increasing in indices such as the mean, variance, and lift, indicating a trajectory that proceeds across multiple years

of Struma Ovarii: A Rare Ovarian Tumor

Abstract Struma ovarii or monodermal teratoma is a specialized ovarian neoplasm which mainly constitutes mature thyroid tissue. It is a rare tumor which comprises 1% of all ovarian tumors and 2.7% of all dermoid tumors. Thyroid tissue can be observed in 5-15% of dermoid tumors but to designate the tumor as struma ovarii, it must comprise more than 50% of the ovarian tissue. This study was conducted in the Department of Pathology, Manipal Teaching Hospital in Pokhara, Nepal over a period of 10 years (Jan 2006 to Sep 2015. Age, clinical findings, preoperative imaging diagnosis, size and side of the tumor, gross and microscopic findings along with type of surgery performed are included in the study. During this 10 years period, there were 7 cases of struma ovarii with age ranging from 26 to 56 years. 2 cases had tumor on the right and 4 cases had tumor on the left side while 1 case had bilateral struma ovarii. Initial presenting symptom was palpable mass, abdominal pain and vaginal bleeding. The size of the tumor ranged from 4 to 15 cm. The capsule was smooth and cut surface shows multiloculated cyst filled with greenish to pale brown gelatinous thick fluid. Microscopic examination revealed well encapsulated tumor composed entirely of thyroid follicles. Diagnoses of struma ovarii were made in all cases. The preoperative imaging may not exactly give the diagnosis. Clinically, lesser age group was more affected and left side is more commonly involved in our series, in contrary to other literature. Out of 7 cases, bilateral struma ovarii was seen in 1 patient. No malignant features were seen in any of these cases

Extending the DMSP Nighttime Lights Time Series beyond 2013

Data collected by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors have been archived and processed by the Earth Observation Group (EOG) at the National Oceanic and Atmospheric Administration (NOAA) to make global maps of nighttime images since 1994. Over the years, the EOG has developed automatic algorithms to make Stable Lights composites from the OLS visible band data by removing the transient lights from fires and fishing boats. The ephemeral lights are removed based on their high brightness and short duration. However, the six original satellites collecting DMSP data gradually shifted from day/night orbit to dawn/dusk orbit, which is to an earlier overpass time. At the beginning of 2014, the F18 satellite was no longer collecting usable nighttime data, and the focus had shifted to processing global nighttime images from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data. Nevertheless, it was soon discovered that the F15 and F16 satellites had started collecting pre-dawn nighttime data from 2012 onwards. Therefore, the established algorithms of the previous years were extended to process OLS data from 2013 onwards. Moreover, the existence of nighttime data from three overpass times for the year 2013–DMSP satellites F18 and F15 from early evening and pre-dawn, respectively, and the VIIRS from after midnight, made it possible to intercalibrate the images of three different overpass times and study the diurnal pattern of nighttime lights

The Dimming of Lights in India during the COVID-19 Pandemic

The monthly Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) composite reveals the dimming of lights as an effect of the lockdown enforced by the government of India in response to the COVID-19 pandemic. The changes in lighting are examined by creating difference maps of a pre-pandemic pair and comparing it with two pandemic pairs. The visual raster difference maps are substantiated with quantitative analysis showing the proportion of population affected by the changes in the lighting brightness levels. In the pre-pandemic images of February and March 2019, 60% of the population lived in administrative units that became brighter in March 2019. However, in the first pandemic pair, 87% of the population lived in administrative units that became dimmer in March 2020 after the lockdown in comparison to February 2020. The nightly DNB profile at the airport in Delhi illustrate how the dimming of lights coincide with the date of the onset of the lockdown (in March 2020). The study shows the usefulness of the DNB nightly and monthly composites in examining economic impacts of the pandemic as countries throughout the world go through economic declines and move towards recovery