Case-Crossover Spatial Lag Grid Differences Between Aerosol Optical Depth-PM2.5 and Respiratory-Cardiovascular Emergency Department Visits and Inpatient Hospitalizations in Baltimore, Maryland, USA: Identification of Homogeneous Spatial Areas

Optimal use of aerosol optical depth (AOD)-PM2.5 fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytic method is currently available to evaluate the spatial dimension. The temporal case-crossover design was modified to assess the association between Community Multiscale Air Quality (CMAQ) lag grids and four respiratory-cardiovascular hospital events. The maximum number of adjacent lag grids with the expo-sure-health outcome association determined the size of the homogeneous spatial area. The largest homogeneous spatial area included 5 grids (720 km2) and the smallest 2 grids (288 km2). PMC and PMCK analyses of ED asthma, IP asthma, IP MI, and IP HF were significantly higher in rural grids without air monitors than in urban with air monitors at lag grids 0, 1, and 01. Grids without air monitors had higher AOD-PM2.5 concentration levels, poverty percent, population density, and environmental hazards than grids with air monitors. ED asthma, IP MI, and HF PMCK ORs were significantly higher during the warm season than during the cold season at lag grids 0, 1, 01, and 04. The possibility of elevated fine PM and other demographic and environmental risk factors contributing to elevated respiratory-cardiovascular diseases in persons residing in rural areas was discussed.

Compressive nano-FTIR chemical mapping

Nano-Fourier-transform infrared spectroscopy (nano-FTIR) combines infrared spectroscopy with scanning probe microscopy (SPM) techniques and enables spectroscopic imaging of molecular and electronic properties of matter at nanometer spatial resolution. The spectroscopic imaging can be used to derive chemical mappings, i.e., the spatial distribution of concentrations of the species contained in a given sample. However, due to the sequential scanning principle underlying SPM, recording the complete spectrum over a large spatial area leads to long measurement times. Furthermore, the acquired spectrum often contains additional signals from species and lineshape effects that are not explicitly accounted for. A compressive chemical mapping approach is proposed for undersampled nano-FTIR data that utilizes sparsity of these additional signals in the spectral domain. The approach combines a projection technique with standard compressed sensing, followed by a spatially regularized regression. Using real nano-FTIR measurements superimposed by simulated interferograms representing the chemical mapping of the contained species, it is demonstrated that the proposed procedure performs well even in cases in which the simulated interferograms and the sparse additional signals exhibit a strong spectral overlap.

Uncovering dynamic textual topics that explain crime

Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain. Some of these techniques may provide insights for understanding crime and disorder, especially, via interpreting the weights for the spatial covariates based on regression modelling. However, to date, the use of temporal covariates for the time domain has not played a significant role in the analysis. In this work, we collect time-stamped crime-related news articles, infer crime topics or themes based on the collection and associate the topics with the historical numeric crime counts. We provide a proof-of-concept study, where instead of adopting spatial covariates, we focus on temporal (or dynamic) covariates and assess their utility. We present a novel joint model tailored for the crime articles and counts such that the temporal covariates (latent variables, more generally) are inferred based on the data sources. We apply the model for violent crime in London.

Coastal Community Perspective, Waste Density, and Spatial Area toward Sustainable Waste Management (Case Study: Ambon Bay, Indonesia)

The coastal area has experienced significant changes of waste problems over the past few years. To resolve the waste problems in coastal areas, an understanding of community perception is needed to support government efforts. Therefore, this study aims to review people’s perspectives on the dynamics of waste in the coastal areas. Community perception data were compiled through semi-structured interviews with the surrounding communities in coastal areas. ArcGIS and load count analysis were used to analyze the waste density. Waste was collected from the coastal area in Ambon Bay and analyzed using waste density calculation and spatial analysis. The results show that the total waste density obtained at the coastal area of Ambon Bay is 0.249 kg/m2, of which 0.078 kg/m2 is the density of plastic waste, and 0.171 kg/m2 is the density of non-plastic waste. Communities in coastal areas have made efforts to deal with waste problems, but the efforts made are still ineffective in overcoming these problems. That problem happens because there is a lack of knowledge of the community and lack of infrastructure in coastal areas. The research results have the potential for replication in other coastal areas and are used as the basis of decision making for waste management improvement.

Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa

<div>In this research we use data from a number of different sources of satellite imagery. Below we describe and visualize various metrics of the datasets being considered. Satellite imagery is retrieved from Google earth which is supported by Data SIO (Scripps Institution of Oceanography), NOAA (National Oceanic and Atmospheric Administration), US. Navy (United States Navy), NGA (National Geospatial-Intelligence Agency), GEBCO (General Bathymetric Chart of the Oceans), Image Landsat, and Image IBCAO (International Bathymetric Chart of the Arctic Ocean). Using random sampling of spatial area in Kigali per target area, 342,843 thousands images were retrieved under the five categories: residential high income (78941), residential low income(162501), residential middle income(101401), commercial building, (67400) and industrial zone,(24400). For the industrial zone, we also included some images from Nairobi, Kenya industrial spatial area. The average number of samples for a category is 86929. The size of the sample per category is proportional to the size of the spatial target area considered per category. Kigali is located at latitude:-1.985070 and longitude:-1.985070, coordinates. Nairobi is located at latitude:-1.286389 and longitude:36.817223, coordinates.</div>

Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa

<div>In this research we use data from a number of different sources of satellite imagery. Below we describe and visualize various metrics of the datasets being considered. Satellite imagery is retrieved from Google earth which is supported by Data SIO (Scripps Institution of Oceanography), NOAA (National Oceanic and Atmospheric Administration), US. Navy (United States Navy), NGA (National Geospatial-Intelligence Agency), GEBCO (General Bathymetric Chart of the Oceans), Image Landsat, and Image IBCAO (International Bathymetric Chart of the Arctic Ocean). Using random sampling of spatial area in Kigali per target area, 342,843 thousands images were retrieved under the five categories: residential high income (78941), residential low income(162501), residential middle income(101401), commercial building, (67400) and industrial zone,(24400). For the industrial zone, we also included some images from Nairobi, Kenya industrial spatial area. The average number of samples for a category is 86929. The size of the sample per category is proportional to the size of the spatial target area considered per category. Kigali is located at latitude:-1.985070 and longitude:-1.985070, coordinates. Nairobi is located at latitude:-1.286389 and longitude:36.817223, coordinates.</div>

Reproducibility of global electrical heterogeneity measurements on 12-lead ECG: The Multi-Ethnic Study of Atherosclerosis.

Objective: Vectorcardiographic (VCG) global electrical heterogeneity (GEH) metrics showed clinical usefulness. We aimed to assess the reproducibility of GEH metrics. Methods: GEH was measured on two 10-second 12-lead ECGs recorded on the same day in 4,316 participants of the Multi-Ethnic Study of Atherosclerosis (age 69.4 ± 9.4 y; 2317(54%) female, 1728 (40%) white, 1138(26%) African-American, 519(12%) Asian-American, 931(22%) Hispanic-American). GEH was measured on a median beat, comprised of the normal sinus (N), atrial fibrillation/flutter (S), and ventricular-paced (VP) beats. Spatial ventricular gradient's (SVG's) scalar was measured as sum absolute QRST integral (SAIQRST) and vector magnitude QT integral (VMQTi). Results: Two N ECGs with heart rate (HR) bias of -0.64 (95% limits of agreement [LOA] -5.68 to 5.21) showed spatial area QRS-T angle (aQRST) bias of -0.12 (95%LOA -14.8 to 14.5). Two S ECGs with HR bias of 0.20 (95%LOA -15.8 to 16.2) showed aQRST bias of 1.37 (95%LOA -33.2 to 35.9). Two VP ECGs with HR bias of 0.25 (95%LOA -3.0 to 3.5) showed aQRST bias of -1.03 (95%LOA -11.9 to 9.9). After excluding premature atrial or ventricular beat and two additional beats (before and after extrasystole), the number of cardiac beats included in a median beat did not affect the GEH reproducibility. Mean-centered log-transformed values of SAIQRST and VMQTi demonstrated perfect agreement (Bias 0; 95%LOA -0.092 to 0.092). Conclusion: GEH measurements on N, S, and VP median beats are reproducible. SVG's scalar can be measured as either SAIQRST or VMQTi. Significance: Satisfactory reproducibility of GEH metrics supports their implementation.

Teknologi Drone untuk Estimasi Stok Karbon di Area Mangrove Pulau Kemujan, Karimunjawa

Hutan mangrove dapat mengurangi emisi karbon dengan menyerap CO2 yang berasal dari udara. Kawasan Tracking Mangrove Pulau Kemujan merupakan salah satu pulau di Taman Nasional Karimunjawa yang didominasi oleh mangrove. Jumlah serapan karbon yang tersimpan di mangrove perlu dihitung sebagai upaya penanganan iklim global dan menambah fungsi mangrove. Pendugaan karbon dapat dilakukan melalui teknologi penginderaan jauh, salah satunya dengan drone. Tujuan dari penelitian ini adalah menghitung dan memetakan area spasial distribusi stok karbon di area tracking mangrove Pulau Kemujan, Karimunjawa. Penelitian ini menggunakan data kuantitatif yang didapatkan dari pendekatan analisis spasial dan data pengukuran lapangan. Metode yang diaplikasikan terdiri dari fotogrametri, image classification, dan perhitungan pendugaan karbon. Resolusi hasil foto udara adalah 3,19 cm/pix, uji korelasi dan uji validasi antara nilai karbon dan indeks vegetasi (NDVI) adalah 0,658 dan 10,738%. Hasil penelitian menunjukkan bahwa area tracking mangrove Pulau Kemujan, Karimunjawa memiliki estimasi simpanan karbon antara 8,42–224,6 ton/ha, dominansi karbon tertinggi berkisar antara 19,43-31,20 ton/ha yang mencakup 8,159 ha. Total area yang terpetakan adalah 28,462 ha dengan rata -rata nilai karbon 56,93 ton/ha. Mangrove forests can reduce carbon emissions by absorbing CO2 from the air. Mangrove Tracking Area of Kemujan Island is one of the islands in Karimunjawa National Park which dominated by mangroves. The amount of carbon sequestration in mangroves needs to be calculated in order to reduce the climate change impact and increase the function of mangroves. Carbon estimation could be approached by remote sensing technology, drones are one of them. The study aims to calculate carbon sequestration and mapping the spatial area of carbon stock distribution in the mangrove tracking area of Kemujan Island, Karimunjawa. Quantitative data are obtained from the spatial analysis and field measurement data. The method applied consists of photogrammetry, image classification, and calculation of carbon estimation. Resolution of aerial photo is 3.19 cm/pix, correlation test and validation test between carbon value and vegetation index (NDVI) are 0.658 and 10.738%, respectively. The result showed that the mangrove tracking area of Kemujan Island, Karimunjawa had an estimated of carbon stock ranges from 8.42–224.6 tons/ha, the highest dominance is 19.43-31.20 tons/ha which covered 8,159 ha. The total area mapped as a spatial area of carbon stock distribution is 28,462 ha with an average carbon value of 56.93 tons/ha.