Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data have been collected that provides an indication of a sensors’ health. These telemetry data were analyzed over an 18-month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this.

Using side-facing observations of the New York City (NYC) skyline, we identify lighting technologies via spectral signatures measured with Visible and Near Infrared (VNIR) hyperspectral imaging. The instrument is a scanning, single slit spectrograph with 872 spectral channels from 0.4–1.0 $\mathsf{\mu }$ m. With a single scan, we are able to clearly match the detected spectral signatures of 13 templates of known lighting types. However, many of the observed lighting spectra do not match those that have been measured in the laboratory. We identify unknown spectra by segmenting our observations and using Template-Activated Partition (TAP) clustering with a variety of underlying unsupervised clustering methods to generate the first empirically-determined spectral catalog of roughly 40 urban lighting types. We show that, given our vantage point, we are able to determine lighting technology use for both interior and exterior lighting. Finally, we find that the total brightness of our scene shows strong peaks at the 570 nm $\mathrm{Na}$ - $\mathrm{II}$ , 595 nm $\mathrm{Na}$ - $\mathrm{II}$ and 818 nm $\mathrm{Na}$ - $\mathrm{I}$ lines that are common in high pressure sodium lamps, which dominate our observations.

We describe an “Urban Observatory” facility designed for the study of complex urban systems via persistent, synoptic, and granular imaging of dynamical processes in cities. An initial deployment of the facility has been demonstrated in New York City and consists of a suite of imaging systems—both broadband and hyperspectral—sensitive to wavelengths from the visible (∼400 nm) to the infrared (∼13 micron) operating at cadences of ∼0.01–30 Hz (characteristically ∼0.1 Hz). Much like an astronomical survey, the facility generates a large imaging catalog from which we have extracted observables (e.g., time-dependent brightnesses, spectra, temperatures, chemical species, etc.), collecting them in a parallel source catalog. We have demonstrated that, in addition to the urban science of cities as systems, these data are applicable to a myriad of domain-specific scientific inquiries related to urban functioning including energy consumption and end use, environmental impacts of cities, and patterns of life and public health. We show that an Urban Observatory facility of this type has the potential to improve both a city’s operations and the quality of life of its inhabitants.

We examine the impact of changes in ozone (O₃), particulate matter (PM_2.5), temperature, and humidity on the health of vegetation in dense urban environments, using a very high-resolution, ground-based Visible and Near-Infrared (VNIR, 0.4–1.0 μm with a spectral resolution of 0.75 nm) hyperspectral camera deployed by the Urban Observatory (UO) in New York City. Images were captured at 15 min intervals from 08h00 to 18h00 for 30 days between 3 May and 6 June 2016 with each image containing a mix of dense built structures, sky, and vegetation. Vegetation pixels were identified using unsupervised k-means clustering of the pixel spectra and the time dependence of the reflection spectrum of a patch of vegetation at roughly 1 km from the sensor that was measured across the study period. To avoid illumination and atmospheric variability, we introduce a method that measures the ratio of vegetation pixel spectra to the spectrum of a nearby building surface at each time step relative to that ratio at a fixed time. This “Compound Ratio” exploits the (assumed) static nature of the building reflectance to isolate the variability of vegetation reflectance. Two approaches are used to quantify the health of vegetation at each time step: (a) a solar-induced fluorescence indicator ($SIFi$) calculated as the simple ratio of the amplitude of the Compound Ratio at 0.75 μm and 0.9 μm, and (b) Principal Component Analysis (PCA) decomposition designed to capture more global spectral features. The time dependence of these vegetation health indicators is compared to that of O₃, PM_2.5, temperature, and humidity values from a distributed and publicly available in situ air quality sensor network. Assuming a linear relationship between vegetation health indicators and air quality indicators, we find that changes in both SIF indicator values and PC amplitudes show a strong correlation ($r^2$ value of 40% and 47%, respectively) with changes in air quality, especially in comparison with nearby buildings used as controls ($r^2$ value of 1% and 4%, respectively, and with all molecular correlations consistent with zero to within 3$\sigma$ uncertainty). Using the SIF indicator, O₃ and temperature exhibit a positive correlation with changes in photosynthetic rate in vegetation, while PM_2.5 and humidity exhibit a negative correlation. We estimate full covariant uncertainties on the coefficients using a Markov Chain Monte Carlo (MCMC) approach and demonstrate that these correlations remain statistically significant even when controlling for the effects of diurnal sun-sensor geometry and temperature variability. This work highlights the importance of quantifying the effects of various air quality parameters on vegetation health in urban environments in order to uncover the complexity, covariance, and interdependence of the numerous factors involved.

Replacing inefficient lighting with energy-efficient alternatives is a proven way to reduce urban energy use, yet evaluating such policies remains challenging. For example, in 2013, New York City (NYC) initiated a program to replace 250,000 high-pressure sodium (HPS) streetlights with light-emitting diodes (LEDs) by 2017, but no subsequent evaluation was published. Here, we employ ground-based hyperspectral imaging (HSI; 0.4–1.0 microns, ∼850 bands) observations from the “Urban Observatory” (UO), obtained in 2013 and 2018, to quantitatively characterize this technological transition. Following co-registration, artifact removal, and source identification, we classified individual light source technologies using both a maximum correlation approach with spectral templates of known lighting types and a one-dimensional Convolutional Neural Network (1D-CNN) trained on 1321 manually labeled spectra, achieving an average precision of ∼92% for the 2013 data and ∼94% for the 2018 data across technology classes. Scene-level mixture modeling indicates a reduction in the HPS-to-LED brightness ratio from 1.15 (2013) to 0.27 (2018), demonstrating the capability of longitudinal HSI for evaluating urban lighting policy outcomes.