Hasil untuk "Maps"

Josh Harris

This paper gives a perspective about how Google Maps, one of the world’s most influential application works. Google Maps was initially coded in C++ programming language by its founders - Lars and Jens Eilstrup Rasmussen. Formerly it was named ‘Where 2 Technologies’, which was later acquired by Google Inc. in 2004, which renamed this web-application to Google Maps. Earlier it had limited features restricted to navigation, but today it provides overwhelming features like street-view, ETA and other such intriguing features. It gives an overview about the algorithms and procedures employed by Google Maps to carry out analysis and enable users to carry out desired operations. Various features provided by Google Maps are portrayed in this paper. It describes the algorithms and procedures used by Google Maps to find the shortest path, locate one’s position, geocoding and other such elegant features it provides its users.

R. Margolin, Lihi Zelnik-Manor, A. Tal

Kegang Hua, Jiangyang Zhang, S. Wakana et al.

V. Paulsen

R. Coifman, Stéphane Lafon, Ann B. Lee et al.

P. Moran

Elisabeth S. Nelson

D. Scharstein, R. Szeliski

M. Gromov

Hans P. Moravec, A. Elfes

J. Fridrich

P. Stam

J. Anderson, G. Churchill, J. E. Autrique et al.

Liang Wang, Ryan E. B. Mruczek, Michael Arcaro et al.

Isomi M. Miake-Lye, S. Hempel, R. Shanman et al.

BackgroundThe need for systematic methods for reviewing evidence is continuously increasing. Evidence mapping is one emerging method. There are no authoritative recommendations for what constitutes an evidence map or what methods should be used, and anecdotal evidence suggests heterogeneity in both. Our objectives are to identify published evidence maps and to compare and contrast the presented definitions of evidence mapping, the domains used to classify data in evidence maps, and the form the evidence map takes.MethodsWe conducted a systematic review of publications that presented results with a process termed “evidence mapping” or included a figure called an “evidence map.” We identified publications from searches of ten databases through 8/21/2015, reference mining, and consulting topic experts. We abstracted the research question, the unit of analysis, the search methods and search period covered, and the country of origin. Data were narratively synthesized.ResultsThirty-nine publications met inclusion criteria. Published evidence maps varied in their definition and the form of the evidence map. Of the 31 definitions provided, 67 % described the purpose as identification of gaps and 58 % referenced a stakeholder engagement process or user-friendly product. All evidence maps explicitly used a systematic approach to evidence synthesis. Twenty-six publications referred to a figure or table explicitly called an “evidence map,” eight referred to an online database as the evidence map, and five stated they used a mapping methodology but did not present a visual depiction of the evidence.ConclusionsThe principal conclusion of our evaluation of studies that call themselves “evidence maps” is that the implied definition of what constitutes an evidence map is a systematic search of a broad field to identify gaps in knowledge and/or future research needs that presents results in a user-friendly format, often a visual figure or graph, or a searchable database. Foundational work is needed to better standardize the methods and products of an evidence map so that researchers and policymakers will know what to expect of this new type of evidence review.Systematic review registrationAlthough an a priori protocol was developed, no registration was completed; this review did not fit the PROSPERO format.

C. Schulp, D. Landuyt

B. Schwalb, Margaux Michel, Benedikt Zacher et al.

G. Green

The dustmaps package provides a uniform Python interface for several commonly used maps of interstellar dust, including two-dimensional maps such as Schlegel, Finkbeiner, and Davis (1998), Planck Collaboration et al. (2014) and Lenz, Hensley, and Doré (2017), and three-dimensional maps such as Marshall et al. (2006) and Green et al. (2015). dustmaps makes use of Astropy’s coordinate-system package (astropy.coordinates.SkyCoord, Astropy Collaboration 2013), making it easy to query dust maps in a wide variety of coordinate systems (Equatorial, Galactic, Ecliptic, etc.). Additionally, dustmaps handles the downloading of the supported dust maps for users, and allows users to query some dust maps from a remote server, avoiding the need to download large data files.

Daniel Aaronson, Daniel A. Hartley, B. Mazumder

This study uses a boundary design and propensity score methods to study the effects of the 1930s-era Home Owners Loan Corporation (HOLC) “redlining” maps on the long-run trajectories of urban neighborhoods. The maps led to reduced home ownership rates, house values, and rents and increased racial segregation in later decades. A comparison on either side of a city-level population cutoff that determined whether maps were drawn finds broadly similar conclusions. These results suggest the HOLC maps had meaningful and lasting effects on the development of urban neighborhoods through reduced credit access and subsequent disinvestment. (JEL G21, J15, N32, N42, N92, R23, R31)

M. Ballarotta, C. Ubelmann, M. Pujol et al.

Abstract. The Data Unification and Altimeter Combination System (DUACS) produces sea level global and regional maps that serve oceanographic applications, climate forecasting centers, and geophysics and biology communities. These maps are generated using an optimal interpolation method applied to altimeter observations. They are provided on a global 1∕4∘ × 1∕4∘ (longitude × latitude) and daily grid resolution framework (1∕8∘ × 1∕8∘ longitude × latitude grid for the regional products) through the Copernicus Marine Environment Monitoring Service (CMEMS). Yet, the dynamical content of these maps does not have full 1∕4∘ spatial and 1 d temporal resolutions due to the filtering properties of the optimal interpolation. In the present study, we estimate the effective spatial and temporal resolutions of the newly reprocessed delayed-time DUACS maps (a.k.a. DUACS-DT2018). Our approach is based on the ratio between the spectral content of the mapping error and the spectral content of independent true signals (along-track and tide gauge observations), also known as the noise-to-signal ratio. We found that the spatial resolution of the DUACS-DT2018 global maps based on sampling by three altimeters simultaneously ranges from ∼100 km wavelength at high latitude to ∼800 km wavelength in the equatorial band and the mean temporal resolution is ∼34 d. The mean effective spatial resolution at midlatitude is estimated to be ∼200 km. The mean effective spatial resolution is ∼130 km for the regional Mediterranean Sea and for the regional Black Sea products. An intercomparison with previous DUACS reprocessing systems (a.k.a., DUACS-DT2010 and DUACS-DT2014) highlights the progress of the system over the past 8 years, in particular a gain of resolution in highly turbulent regions. The same diagnostic applied to maps constructed with two altimeters and maps with three altimeters confirms a modest increase in resolving capabilities and accuracies in the DUACS maps with the number of missions.