14,304 research outputs found
From buildings to cities: techniques for the multi-scale analysis of urban form and function
The built environment is a significant factor in many urban processes, yet direct measures of built form are
seldom used in geographical studies. Representation and analysis of urban form and function could provide
new insights and improve the evidence base for research. So far progress has been slow due to limited data
availability, computational demands, and a lack of methods to integrate built environment data with
aggregate geographical analysis. Spatial data and computational improvements are overcoming some of
these problems, but there remains a need for techniques to process and aggregate urban form data. Here we
develop a Built Environment Model of urban function and dwelling type classifications for Greater
London, based on detailed topographic and address-based data (sourced from Ordnance Survey
MasterMap). The multi-scale approach allows the Built Environment Model to be viewed at fine-scales for
local planning contexts, and at city-wide scales for aggregate geographical analysis, allowing an improved
understanding of urban processes. This flexibility is illustrated in the two examples, that of urban function
and residential type analysis, where both local-scale urban clustering and city-wide trends in density and
agglomeration are shown. While we demonstrate the multi-scale Built Environment Model to be a viable
approach, a number of accuracy issues are identified, including the limitations of 2D data, inaccuracies in
commercial function data and problems with temporal attribution. These limitations currently restrict the
more advanced applications of the Built Environment Model
Pilot investigation of remote sensing for intertidal oyster mapping in coastal South Carolina: a methods comparison
South Carolina’s oyster reefs are a major component of the coastal landscape. Eastern oysters Crassostrea virginica are an important economic resource to the state and serve many essential functions in the environment, including water filtration, creek bank stabilization and habitat for
other plants and animals. Effective conservation and management of oyster reefs is dependent on an understanding of their abundance, distribution, condition, and change over time. In South Carolina, over 95% of the state’s oyster habitat is intertidal. The current intertidal oyster reef database for South Carolina was developed by field assessment over several years. This database was completed in the early 1980s and is in need of an update to assess resource/habitat status and trends across the state. Anthropogenic factors such as coastal development and
associated waterway usage (e.g., boat wakes) are suspected of significantly altering the extent and health of the state’s oyster resources.
In 2002 the NOAA Coastal Services Center’s (Center) Coastal Remote Sensing Program (CRS) worked with the Marine Resources Division of the South Carolina Department of Natural Resources (SCDNR) to develop methods for mapping intertidal oyster reefs along the South Carolina coast using remote sensing technology. The objective of this project was to provide SCDNR with potential methodologies and approaches for assessing oyster resources in a more
efficiently than could be accomplished through field digitizing. The project focused on the utility of high-resolution aerial imagery and on documenting the effectiveness of various analysis techniques for accomplishing the update. (PDF contains 32 pages
Distribution of Husimi Zeroes in Polygonal Billiards
The zeroes of the Husimi function provide a minimal description of individual
quantum eigenstates and their distribution is of considerable interest. We
provide here a numerical study for pseudo- integrable billiards which suggests
that the zeroes tend to diffuse over phase space in a manner reminiscent of
chaotic systems but nevertheless contain a subtle signature of
pseudo-integrability. We also find that the zeroes depend sensitively on the
position and momentum uncertainties with the classical correspondence best when
the position and momentum uncertainties are equal. Finally, short range
correlations seem to be well described by the Ginibre ensemble of complex
matrices.Comment: includes 13 ps figures; Phys. Rev. E (in press
Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks
Information garnered from activity on location-based social networks can be
harnessed to characterize urban spaces and organize them into neighborhoods. In
this work, we adopt a data-driven approach to the identification and modeling
of urban neighborhoods using location-based social networks. We represent
geographic points in the city using spatio-temporal information about
Foursquare user check-ins and semantic information about places, with the goal
of developing features to input into a novel neighborhood detection algorithm.
The algorithm first employs a similarity metric that assesses the homogeneity
of a geographic area, and then with a simple mechanism of geographic
navigation, it detects the boundaries of a city's neighborhoods. The models and
algorithms devised are subsequently integrated into a publicly available,
map-based tool named Hoodsquare that allows users to explore activities and
neighborhoods in cities around the world.
Finally, we evaluate Hoodsquare in the context of a recommendation
application where user profiles are matched to urban neighborhoods. By
comparing with a number of baselines, we demonstrate how Hoodsquare can be used
to accurately predict the home neighborhood of Twitter users. We also show that
we are able to suggest neighborhoods geographically constrained in size, a
desirable property in mobile recommendation scenarios for which geographical
precision is key.Comment: ASE/IEEE SocialCom 201
V-Proportion: a method based on the Voronoi diagram to study spatial relations in neuronal mosaics of the retina
The visual system plays a predominant role in the human perception. Although all components of the eye are important to perceive visual information, the retina is a fundamental part of the visual system. In this work we study the spatial relations between neuronal mosaics in the retina. These relations have shown its importance to investigate possible constraints or connectivities between different spatially colocalized populations of neurons, and to explain how visual information spreads along the layers before being sent to the brain. We introduce the V-Proportion, a method based on the Voronoi diagram to study possible spatial interactions between two neuronal mosaics. Results in simulations as well as in real data demonstrate the effectiveness of this method to detect spatial relations between neurons in different layers
Impact of the spatial context on human communication activity
Technology development produces terabytes of data generated by hu- man
activity in space and time. This enormous amount of data often called big data
becomes crucial for delivering new insights to decision makers. It contains
behavioral information on different types of human activity influenced by many
external factors such as geographic infor- mation and weather forecast. Early
recognition and prediction of those human behaviors are of great importance in
many societal applications like health-care, risk management and urban
planning, etc. In this pa- per, we investigate relevant geographical areas
based on their categories of human activities (i.e., working and shopping)
which identified from ge- ographic information (i.e., Openstreetmap). We use
spectral clustering followed by k-means clustering algorithm based on TF/IDF
cosine simi- larity metric. We evaluate the quality of those observed clusters
with the use of silhouette coefficients which are estimated based on the
similari- ties of the mobile communication activity temporal patterns. The area
clusters are further used to explain typical or exceptional communication
activities. We demonstrate the study using a real dataset containing 1 million
Call Detailed Records. This type of analysis and its application are important
for analyzing the dependency of human behaviors from the external factors and
hidden relationships and unknown correlations and other useful information that
can support decision-making.Comment: 12 pages, 11 figure
Enhanced free space detection in multiple lanes based on single CNN with scene identification
Many systems for autonomous vehicles' navigation rely on lane detection.
Traditional algorithms usually estimate only the position of the lanes on the
road, but an autonomous control system may also need to know if a lane marking
can be crossed or not, and what portion of space inside the lane is free from
obstacles, to make safer control decisions. On the other hand, free space
detection algorithms only detect navigable areas, without information about
lanes. State-of-the-art algorithms use CNNs for both tasks, with significant
consumption of computing resources. We propose a novel approach that estimates
the free space inside each lane, with a single CNN. Additionally, adding only a
small requirement concerning GPU RAM, we infer the road type, that will be
useful for path planning. To achieve this result, we train a multi-task CNN.
Then, we further elaborate the output of the network, to extract polygons that
can be effectively used in navigation control. Finally, we provide a
computationally efficient implementation, based on ROS, that can be executed in
real time. Our code and trained models are available online.Comment: Will appear in the 2019 IEEE Intelligent Vehicles Symposium (IV 2019
Galaxy clustering and projected density profiles as traced by satellites in photometric surveys: Methodology and luminosity dependence
We develop a new method which measures the projected density distribution
w_p(r_p)n of photometric galaxies surrounding a set of
spectroscopically-identified galaxies, and simultaneously the projected
correlation function w_p(r_p) between the two populations. In this method we
are able to divide the photometric galaxies into subsamples in luminosity
intervals when redshift information is unavailable, enabling us to measure
w_p(r_p)n and w_p(r_p) as a function of not only the luminosity of the
spectroscopic galaxy, but also that of the photometric galaxy. Extensive tests
show that our method can measure w_p(r_p) in a statistically unbiased way. The
accuracy of the measurement depends on the validity of the assumption in the
method that the foreground/background galaxies are randomly distributed and
thus uncorrelated with those galaxies of interest. Therefore, our method can be
applied to the cases where foreground/background galaxies are distributed in
large volumes, which is usually valid in real observations. We applied our
method to data from SDSS including a sample of 10^5 LRGs at z~0.4 and a sample
of about half a million galaxies at z~0.1, both of which are cross-correlated
with a deep photometric sample drawn from the SDSS. On large scales, the
relative bias factor of galaxies measured from w_p(r_p) at z~0.4 depends on
luminosity in a manner similar to what is found at z~0.1, which are usually
probed by autocorrelations of spectroscopic samples. On scales smaller than a
few Mpc and at both z~0.4 and z~0.1, the photometric galaxies of different
luminosities exhibit similar density profiles around spectroscopic galaxies at
fixed luminosity and redshift. This provides clear support for the assumption
commonly-adopted in HOD models that satellite galaxies of different
luminosities are distributed in a similar way, following the dark matter
distribution within their host halos.Comment: 38 pages, 12 figures, published in Ap
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