19,724 research outputs found
Combinatorial locational analysis of public services in metropolitan areas. Case study in the city of Volos, Greece.
Social prosperity largely depends on spatial structure, a relation which becomes stronger in urban areas where the quality of life is menaced by several factors. Traffic, over-building, lack of open space and deficient location of services come to the fore. The latter reflects access inequality and is one of the main reasons for everyday movement difficulties of citizens. Particularly, public services, as part of the public sector, are considered to be driven by the principle of social well-fare. Therefore the study of their location gives rise to the question: how can access of city blocks to public services be evaluated and how can the results of this evaluation be combined with the monetary values assigned by the state? In this respect, the main aim of this paper is the determination of a synthetic methodological framework for the locational analysis and evaluation of public services in urban areas. The proposed approach is based on spatial analysis methods and techniques as well as on the analytical capabilities of GIS and finally leads to the definition of the locational value for each city block. The public services are classified according to served population age groups and to their yearly utilization levels. The minimum and average Manhattan distances to the services of each classification group are calculated along with the percentages of services that are closer than a critical radius to each city block. At the final step, city blocks are classified through the use of cluster analysis to the calculated distances and percentages and then ranked according to their overall accessibility to public services. Their score is utilized in the definition of their locational value and in the formulation of a combinatorial index which compares locational and land values throughout the study area. The methodological framework is applied in the city of Volos where according to the results of the analytical process the majority of city blocks (60,7%) indicates a comparatively lower locational than monetary land value.
On central tendency and dispersion measures for intervals and hypercubes
The uncertainty or the variability of the data may be treated by considering,
rather than a single value for each data, the interval of values in which it
may fall. This paper studies the derivation of basic description statistics for
interval-valued datasets. We propose a geometrical approach in the
determination of summary statistics (central tendency and dispersion measures)
for interval-valued variables
Integer Point Sets Minimizing Average Pairwise L1-Distance: What is the Optimal Shape of a Town?
An n-town, for a natural number n, is a group of n buildings, each occupying
a distinct position on a 2-dimensional integer grid. If we measure the distance
between two buildings along the axis-parallel street grid, then an n-town has
optimal shape if the sum of all pairwise Manhattan distances is minimized. This
problem has been studied for cities, i.e., the limiting case of very large n.
For cities, it is known that the optimal shape can be described by a
differential equation, for which no closed-form is known. We show that optimal
n-towns can be computed in O(n^7.5) time. This is also practically useful, as
it allows us to compute optimal solutions up to n=80.Comment: 26 pages, 6 figures, to appear in Computational Geometry: Theory and
Application
Decoding Single Molecule Time Traces with Dynamic Disorder
Single molecule time trajectories of biomolecules provide glimpses into
complex folding landscapes that are difficult to visualize using conventional
ensemble measurements. Recent experiments and theoretical analyses have
highlighted dynamic disorder in certain classes of biomolecules, whose dynamic
pattern of conformational transitions is affected by slower transition dynamics
of internal state hidden in a low dimensional projection. A systematic means to
analyze such data is, however, currently not well developed. Here we report a
new algorithm - Variational Bayes-double chain Markov model (VB-DCMM) - to
analyze single molecule time trajectories that display dynamic disorder. The
proposed analysis employing VB-DCMM allows us to detect the presence of dynamic
disorder, if any, in each trajectory, identify the number of internal states,
and estimate transition rates between the internal states as well as the rates
of conformational transition within each internal state. Applying VB-DCMM
algorithm to single molecule FRET data of H-DNA in 100 mM-Na solution,
followed by data clustering, we show that at least 6 kinetic paths linking 4
distinct internal states are required to correctly interpret the duplex-triplex
transitions of H-DNA
Quantification and Comparison of Degree Distributions in Complex Networks
The degree distribution is an important characteristic of complex networks.
In many applications, quantification of degree distribution in the form of a
fixed-length feature vector is a necessary step. On the other hand, we often
need to compare the degree distribution of two given networks and extract the
amount of similarity between the two distributions. In this paper, we propose a
novel method for quantification of the degree distributions in complex
networks. Based on this quantification method,a new distance function is also
proposed for degree distributions, which captures the differences in the
overall structure of the two given distributions. The proposed method is able
to effectively compare networks even with different scales, and outperforms the
state of the art methods considerably, with respect to the accuracy of the
distance function
Structure in the 3D Galaxy Distribution: I. Methods and Example Results
Three methods for detecting and characterizing structure in point data, such
as that generated by redshift surveys, are described: classification using
self-organizing maps, segmentation using Bayesian blocks, and density
estimation using adaptive kernels. The first two methods are new, and allow
detection and characterization of structures of arbitrary shape and at a wide
range of spatial scales. These methods should elucidate not only clusters, but
also the more distributed, wide-ranging filaments and sheets, and further allow
the possibility of detecting and characterizing an even broader class of
shapes. The methods are demonstrated and compared in application to three data
sets: a carefully selected volume-limited sample from the Sloan Digital Sky
Survey redshift data, a similarly selected sample from the Millennium
Simulation, and a set of points independently drawn from a uniform probability
distribution -- a so-called Poisson distribution. We demonstrate a few of the
many ways in which these methods elucidate large scale structure in the
distribution of galaxies in the nearby Universe.Comment: Re-posted after referee corrections along with partially re-written
introduction. 80 pages, 31 figures, ApJ in Press. For full sized figures
please download from: http://astrophysics.arc.nasa.gov/~mway/lss1.pd
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