Showing papers on "Choropleth map published in 2016"
TL;DR: Heatmapper is a freely available web server that allows users to interactively visualize their data in the form of heat maps through an easy-to-use graphical interface and is designed to appeal to a wide range of users.
Abstract: Heatmapper is a freely available web server that allows users to interactively visualize their data in the form of heat maps through an easy-to-use graphical interface Unlike existing non-commercial heat map packages, which either lack graphical interfaces or are specialized for only one or two kinds of heat maps, Heatmapper is a versatile tool that allows users to easily create a wide variety of heat maps for many different data types and applications More specifically, Heatmapper allows users to generate, cluster and visualize: (i) expression-based heat maps from transcriptomic, proteomic and metabolomic experiments; (ii) pairwise distance maps; (iii) correlation maps; (iv) image overlay heat maps; (v) latitude and longitude heat maps and (vi) geopolitical (choropleth) heat maps Heatmapper offers a number of simple and intuitive customization options for facile adjustments to each heat map's appearance and plotting parameters Heatmapper also allows users to interactively explore their numeric data values by hovering their cursor over each heat map cell, or by using a searchable/sortable data table view Heat map data can be easily uploaded to Heatmapper in text, Excel or tab delimited formatted tables and the resulting heat map images can be easily downloaded in common formats including PNG, JPG and PDF Heatmapper is designed to appeal to a wide range of users, including molecular biologists, structural biologists, microbiologists, epidemiologists, environmental scientists, agriculture/forestry scientists, fish and wildlife biologists, climatologists, geologists, educators and students Heatmapper is available at http://wwwheatmapperca
2,124 citations
TL;DR: In this paper, the authors developed a procedure to use more detailed fiscal cadastre blocks to disaggregate census data within less detailed enumeration and sample areas, which can provide a more accurate and detailed distribution of population data.
Abstract: Choropleth representation has been the most widely applied method to represent rates in disease maps due to its consistency in depicting relative data. However polygons in a choropleth map may give the erroneous notion of homogenous distribution over area in cases where the mapped quantity varies in its spatial distribution. In the case of population maps, choropleth maps suggest uniform distribution of people within large peri-urban administrative areas where population is known to be unevenly distributed within the administrative units. Dasymetric mapping can provide a more accurate and detailed distribution of population data by using ancillary information to spatially disaggregate population within administrative units. We have developed a procedure to use more detailed fiscal cadastre blocks to disaggregate census data within less detailed enumeration and sample areas. Here we explain the procedure and provide simple examples of this dasymetric representation as applied to population density,...
22 citations
TL;DR: In this article, the authors proposed a dasymetric map at a convenient scale with regards to the results of satellite image processing and GIS for Jeddah city (the second largest city in Saudi Arabia).
Abstract: It is well-known that, when dealing with density of population, most of the proposed
maps choose the easiest and probably the most understandable cartographic method,
i.e. the choropleth method. Nevertheless, for heterogonous spaces and those observing
intense spatial dynamic, it is proven that this method has many lacks and deficiencies.
This is the case of Jeddah city (the second largest city in Saudi Arabia),
which is a very contrasted urban place with regards to its social structure, spatial organization
and land use besides the fact that it witnesses a profound and continuous
urban growth. Yet, most of the planning decisions are often taken on these types of
maps and may mislead the urban planners. In this context, the dasymetric maps reveal
very useful because they may give the real distribution of the population. Therefore,
we think that establishing a dasymetric map at a convenient scale with regards
to the results of satellite image processing may help the planners and the geographers
as well as the common users. Indeed, this method may be an interesting alternative
to the classic choropleth map. First it may improve our estimations towards the density
within the various areas of the districts. Second it may refine the original enumeration
units often using the administrative apportionment and therefore help the
planning and agricultural agencies when establishing their base maps. The satellite
image processing and GIS were used as tools in this study.
11 citations
24 May 2016
TL;DR: First examinations proved the logical behavior and desired expressiveness of this index, both numerically (in comparison with a different index in use, the Boundary Accuracy Index) and visually (showing a significant increase in edge information).
Abstract: Beyond many other map-reading tasks by using choropleth maps, the visual detection and comparison of attribute value differences of neighboring polygons are of major interest These tasks are supported not only through an appropriate color scheme, but also through a suitable data classification However, common classification methods perform a division into intervals only along the number line, with that ignoring the required spatial context This is our motivation to present a new measure, the Edge Preservation Index First examinations proved the logical behavior and desired expressiveness of this index, both numerically (in comparison with a different index in use, the Boundary Accuracy Index) and visually (showing a significant increase in edge information)
7 citations
TL;DR: There is not a single data visualization technique that encompasses all the necessary features to visualize prevalence data alone or prevalence data together with their associated uncertainty, and it is recommended to create a dialogue between end‐users and epidemiologists on the basis of sample data and charts.
Abstract: Within the European activities for the 'Monitoring and Collection of Information on Zoonoses', annually EFSA publishes a European report, including information related to the prevalence of Campylobacter spp. in Germany. Spatial epidemiology becomes here a fundamental tool for the generation of these reports, including the representation of prevalence as an essential element. Until now, choropleth maps are the default visualization technique applied in epidemiological monitoring and surveillance reports made by EFSA and German authorities. However, due to its limitations, it seems to be reasonable to explore alternative chart type. Four maps including choropleth, cartogram, graduated symbols and dot-density maps were created to visualize real-world sample data on the prevalence of Campylobacter spp. in raw chicken meat samples in Germany in 2011. In addition, adjacent and coincident maps were created to visualize also the associated uncertainty. As an outcome, we found that there is not a single data visualization technique that encompasses all the necessary features to visualize prevalence data alone or prevalence data together with their associated uncertainty. All the visualization techniques contemplated in this study demonstrated to have both advantages and disadvantages. To determine which visualization technique should be used for future reports, we recommend to create a dialogue between end-users and epidemiologists on the basis of sample data and charts. The final decision should also consider the knowledge and experience of end-users as well as the specific objective to be achieved with the charts.
7 citations
1 Jan 2016
TL;DR: The purpose of this paper is to expand the discussion about incorporating data uncertainty for map classification by extending optimal map classification strategies with Bhattacharyya distance by illustrated with an application of soil lead contamination measurements in the City of Syracuse.
Abstract: Uncertainty in spatial data attributes can produce unreliable spatial patterns in choropleth maps, but only a few studies have considered uncertainty in map classification processes. Unfortunately, a less desirable classification result often is generated by existing methods. For example, most observations are assigned to a single class while the remaining classes have a very small number of observations allocated to them. Also, selection of proper criteria for an optimal map classification is difficult. The purpose of this paper is to expand the discussion about incorporating data uncertainty for map classification by extending optimal map classification strategies with Bhattacharyya distance. The proposed method is illustrated with an application of soil lead contamination measurements in the City of Syracuse.
3 citations
1 Jan 2016
TL;DR: In this paper, a 1:20,000 scale soil map made using physiographic analysis with intensive aerial photo-interpretation of soil-landscape relationships and landscape-oriented field survey was used to adjust the soillandscape/soil-series interpretation of the existing choropleth soil map by correlating discrete productivity index values obtained from a conventional mapping procedure with continuous PI values obtained by soil digital mapping procedures.
Abstract: Research work carried out in Entre-Rios province (Argentina) for mixed land use planning and management in relation to suitable soil conditions required high-resolution soil information at farm level. Basic information was provided by a 1:20,000 scale soil map made using physiographic analysis with intensive aerial photo-interpretation of soil-landscape relationships and landscape-oriented field survey. Continuous productivity-index (PI) classes were predicted from a number of environmental covariates, mostly DEM derivatives, using regression and geostatistical techniques. The PI land classification was used to adjust the soil-landscape/soil-series interpretation of the existing choropleth soil map by means of correlating discrete PI values obtained from a conventional mapping procedure with continuous PI values obtained by soil digital mapping procedures.
3 citations
20 Jun 2016
TL;DR: A system to visualize statistics collected from NLM's MEDLINE® database that contains citations related to biomedical journal articles and Hidden Markov Model (HMM) and statistics are used to extract the information from the affiliations.
Abstract: We propose a system to visualize statistics collected from NLM's MEDLINE® database that contains citations related to biomedical journal articles. The system extracts information from author affiliations in the articles such as organization, city, state, country, etc., categorizes the articles into several groups using the information, collects statistics such as the number of articles published per country each year, etc., and displays the statistics through a Web site using tables and choropleth maps. Hidden Markov Model (HMM) and statistics are used to extract the information from the affiliations, and Google Map API, JSON, JavaScript and other APIs are used for the development of the site.
2 citations
Journal Article•
TL;DR: In this article, the authors proposed to use a single combined, or bivariate legend to improve clarity when comparing relationships, and to avoid less convenient side-by-side legends.
Abstract: Maps representing two variables sometimes use a single combined, or bivariate legend to improve clarity when comparing relationships, and to avoid less convenient side-by-side legends. However, conventional bivariate legends typically omit the underlying bivariate distributions. Using simple scatterplots statistical indicators of covariance can be plotted as points directly on to the choropleth bivariate legend. This allows map readers to not only compare aggregate magnitudes between the two variables but also visualize disaggregate distributions that may represent statistical normality in the data as well as skewness, and linearity. In addition, the covariance distributions can direct class interval selection, or at least inform the reader of which classes represent data abundance or data sparsity. Bivariate statistical legends are tested using examples drawn from Florida population census data at the fine scale of block groups. Practicality is demonstrated by open source software using Data Driven Documents (D3) visual analytic software (http://d3js.org).
1 citations
TL;DR: In this paper, the authors used intelligent areal interpolation to construct two types of population density surfaces that are used as inputs for pycnophylactic interpolation of an isopleth surface.
Abstract: This study reunites areal interpolation with the isopleth mapping process to construct an inferred larger scale isopleth map. Intelligent areal interpolation is used to construct two types of population density surfaces that are used as inputs for pycnophylactic interpolation of an isopleth surface. One is a target zone population density surface (TZPDS) and the other is a control zone population density surface (CZPDS). Results suggest that an inferred isopleth map with remote sensing control data is a better surface depiction than an isopleth map without any control data, and the quality of such an isopleth map is further improved by enhancing the remote sensing data with residential parcel information. A CZPDS-derived intelligent isopleth map also has more peaks and variations in population distribution patterns than does a TZPDS-derived one due to the larger scale of the control data.
1 citations
1 Apr 2016
28 Nov 2016
TL;DR: A novel approach to the hexagonal gridded maps, which integrates diverse information layers with adaptive zoom and the interplay among the complementary graphical layers provided by the visualization increases its exploratory and analytical power.
Abstract: In retail business intelligences, and more specifically in an analysis of customer-supermarket relationship, the factors, such as geographic location of customers, demographic distribution, customers' preferences, accessibility to the store are crucial in decision-making tasks. Visualization is an important tool for analysis and decision-making, which should provide means to make informed business decisions. This article presents a novel approach to the hexagonal gridded maps, which integrates diverse information layers with adaptive zoom. These layers are complementary, providing different points of view over the same dataset and various levels of abstraction. Starting with a dot map, which portrays the impact of supermarket localization on customers choices, up to a choropleth map, which depicts population density in an adaptive form depending on the different granularities of administrative units. Ultimately, the presented visualization provides means to: (i) explore and analyze data regarding customer-supermarket relations; (ii) reveal the impact of supermarkets localization on customer preferences; (iii) suggest areas of low coverage by supermarkets. Additionally, the interplay among the complementary graphical layers provided by the visualization increases its exploratory and analytical power.
Journal Article•
TL;DR: This paper considers some previous theoretical premises and actual examples of aggregation for point, line and polygonal features for geoinformation, and the algorithms for aggregation implemented in commercial and free GIS software were tested.
Abstract: Geoinformation generalization can be divided into model generalization and cartographic generalization. Model generalization is the supervised reduction of data in a model, while cartographic generalization is the reduction of the complexity of map content adapted to the map scale, and/or use by various generalization operators (procedures). The topic of this paper is the aggregation of geoinformation. Generally, aggregation is the joining of nearby, congenial objects, when the distance between them is smaller than the minimum sizes. Most researchers in geoinformation generalization have focused on line features. However, the appearance of web-maps with point features and choropleth maps has led to the development of concepts and algorithms for the generalization of point and polygonal features. This paper considers some previous theoretical premises and actual examples of aggregation for point, line and polygonal features. The algorithms for aggregation implemented in commercial and free GIS software were tested. In the conclusion, unresolved challenges that occur in dynamic cartographic visualizations and cases of unusual geometrical features are highlighted.
TL;DR: In this article, the authors used spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a fl at to improve county level population estimates.
Abstract: Population data are generally provided by state census organisations at the predefi ned census enumeration units. However, these datasets very are often required at userdefi ned spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a fl at. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory.