Measuring the spatial quality of bedrooms in nursing homes with visual environmental performance

The elderly's perception of their environment largely depends on acquired visual information.This study provides a quantitative analytical method for designing bedrooms in nursing homes by developing a computational model that uses viewing distance,viewing angle,and viewing field as variables.The model is utilized to measure a range of visual environmental properties,including privacy,accessibility,permeability,and visibility,which reflect the quality of bedrooms in nursing homes.The suitability levels of bedroom planes for the elderly and their main living functions as well as the fitting degree of the functional layout are calculated based on these data.After validating the computational moders feasibility,this study optimizes the typical schemes of single and double rooms in nursing homes.Results reveal certain advantages of the arrangement where bathroom doors are set toward the inside of the bedroom and double rooms have a large width.The computational model can be used in measuring the spatial quality of bedrooms in nursing homes and as a mathematical model for related algorithm design and software programming to assist in the design and optimization of bedrooms for the elderly.

Modeling data quality for risk assessment of GIS

This paper presents a methodology to determine three data quality (DQ) risk characteristics: accuracy, comprehensiveness and nonmembership. The methodology provides a set of quantitative models to confirm the information quality risks for the database of the geographical information system (GIS). Four quantitative measures are introduced to examine how the quality risks of source information affect the quality of information outputs produced using the relational algebra operations Selection, Projection, and Cubic Product. It can be used to determine how quality risks associated with diverse data sources affect the derived data. The GIS is the prime source of information on the location of cables, and detection time strongly depends on whether maps indicate the presence of cables in the construction business. Poor data quality in the GIS can contribute to increased risk or higher risk avoidance costs. A case study provides a numerical example of the calculation of the trade-offs between risk and detection costs and provides an example of the calculation of the costs of data quality. We conclude that the model contributes valuable new insight.

Visualizing perceived spatial data quality of 3D objects within virtual globes

Virtual globes(VGs)allow Internet users to view geographic data of heterogeneous quality created by other users.This article presents a new approach for collecting and visualizing information about the perceived quality of 3D data in VGs.It aims atimproving users’awareness of the qualityof 3D objects.Instead of relying onthe existing metadata or on formal accuracy assessments that are often impossible in practice,we propose a crowd-sourced quality recommender system based on the five-star visualization method successful in other types of Web applications.Four alternative five-star visualizations were implemented in a Google Earth-based prototype and tested through a formal user evaluation.These tests helped identifying the most effective method for a 3D environment.Results indicate that while most websites use a visualization approach that shows a‘number of stars’,this method was the least preferred by participants.Instead,participants ranked the‘number within a star’method highest as it allowed reducing the visual clutter in urban settings,suggesting that 3D environments such as VGs require different designapproachesthan2Dornon-geographicapplications.Resultsalsoconfirmed that expert and non-expert users in geographic data share similar preferences for the most and least preferred visualization methods.

Long-term agricultural activity affects anthropogenic soil on the Chinese Loess Plateau

Anthropogenic activities largely influence the soil quality of agricultural fields and the composition of soil. Samples of typical anthropogenic Loutu soil in the Guanzhong area of the Loess Plateau, Shaanxi Province, China were collected and measured for soil compaction, bulk density, total organic carbon（TOC）, active organic carbon（AOC）, and soil enzyme activities to investigate spatial variations in soil quality. The results indicate that soil compaction and bulk density increased with increasing distance from the farm village, whereas soil TOC, AOC, and soil enzyme activities firstly increased and subsequently decreased with increasing distance from the farm village. All of the tested parameters presented clear concentric distribution. Vertically, soil compaction and bulk density in the topsoil were lower than those in the subsoil, but all other tested parameters in the topsoil were significantly higher than those in the subsoil. In addition, there was a significant positive correlation between organic carbon content and enzyme activities, confirming that the spatial distribution of Loutu soil characteristics has been affected by long-term anthropogenic activities to some extent. The results of this study imply that the use of farmyard manure and appropriate deep plowing are important and effective ways to maintain and improve soil quality.

Spatial data trusts:an emerging governance framework for sharing spatial data

Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes,backed by the confidence of clear and unambiguous data governance.Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust.The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications,addressing some longstanding barriers to data sharing,such as location privacy and data sovereignty.This paper introduces and explores the concept of a‘spatial data Trust’by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data.The work identifiesfive key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in thefield,including spatial data infrastructures(SDIs),location privacy,and spatial data quality.The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.

Quality assessment of OpenStreetMap data using trajectory mining

OpenStreetMap(OSM)data are widely used but their reliability is still variable.Many contributors to OSM have not been trained in geography or surveying and consequently their contributions,including geometry and attribute data inserts,deletions,and updates,can be inaccurate,incomplete,inconsistent,or vague.There are some mechanisms and applications dedicated to discovering bugs and errors in OSM data.Such systems can remove errors through user-checks and applying predefined rules but they need an extra control process to check the real-world validity of suspected errors and bugs.This paper focuses on finding bugs and errors based on patterns and rules extracted from the tracking data of users.The underlying idea is that certain characteristics of user trajectories are directly linked to the type of feature.Using such rules,some sets of potential bugs and errors can be identified and stored for further investigations.