Environmental public health applications using remotely sensed data

We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM_2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.     

Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data

Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 30 m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) surface reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ and MODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r² = 0.71, p ≤ 0.01) compared to green band (mean r² = 0.53; p ≤ 0.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agreement (r² = 0.85, p ≤ 0.01 and r² = 0.86, p ≤ 0.01) for wheat and sugarcane crops, respectively. The difference between the GPP derived from synthetic and observed ETM+ (prediction residual) was compared with the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.97 g C/m² in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.46 g C/m² in 8 days) that correspondence to 12% and 27%, respectively, of GPP values (mean value of 16.53 g C/m² in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evapotranspiration and synthetic ETM+ GPP showed good agreement (r² = 0.66, p ≤ 0.01).     

Overcoming unreported violence using place-based ambulance data: The case for mapping hotspots based on health data for crime prevention initiatives

A key concern in crime analysis is the “hidden crime” problem. Crime events unaccounted for in police records limit the external validity of official statistics and, more importantly, hinder the ability of the police to manage crime and utilize their resources effectively. The problem is exacerbated in proactive initiatives aimed at curbing violence through hotspot policing, where inaccuracies and imprecision, or, worse, no data at all, diminish prevention efforts. Previous studies have sought to overcome the data problem by juxtaposing police records with ambulance data on assault callouts and have found profound disparities. Specifically, researchers matched “crime hotspots” with “ambulance hotspots” (rather than individual events) because patient confidentiality considerations have prevented health professionals from sharing subject-level data with the police. However, health services can safely share spatial data on wider areas that do not disclose personal information. We build on this line of inquiry by analyzing data from the Thames Valley, United Kingdom, and observing spatial hotspots of different sizes. The results demonstrate that while the police and ambulance services attend to the same communities and similar types of facilities, the police are “blinded” to the location of nearly 8 out of 10 assaults. The incongruency is shown even with severe assaults, but to a lesser extent. We then simulate the reduction in injuries if the police had access to health data at different spatial levels and show that even under the most conservative set of assumptions, such an approach can prevent between 113 and 116 violent injuries each year that might otherwise require hospitalization.     

Mining spatial associations between daily activities and health using EMA–GPS data

The geographical explicit ecological momentary assessment (GEMA) data collection platform provides extremely rich geospatial datasets and is very promising to gain behavior insights linking mobility, activities, and health. However, the task of analyzing these large datasets effectively is not straightforward, because they often involve a large multivariable dimension and rich qualitative data formats. Responding to the call for innovative analytic approaches in GIScience, this article advocates the use of spatial association rule mining (SARM) to extract frequent associations among daily activities, daily mobility, and health, including both physical health (e.g. pain) and mental health (e.g. happiness). This inductive mining approach works robustly with large datasets and is suitable for both qualitative and quantitative studies. A novel visualization technique to analyze the mined rules is also developed and presented.     

重庆市土地利用空间数据库建设及数据发布的研究与应用

在国家第二次土地调查成果数据汇总工作完成后,重庆市将建立全市统一的土地利用空间数据库,同时也要进行数据发布以便于工作成果的各类应用.本文介绍了该市土地利用空间数据库建设及数据发布的工作流程,重点阐述了该项工作中的基础地理空间框架的选取、批量空间数据的坐标转换、DEM数据问题的处理及建模、数据发布等环节的技术处理,可为其...     

Mapping the spatial disparities in urban health care services using taxi trajectories data

The understanding of spatial inequality in health care services is critical for reasonable policy‐making and management. In this study, we present a novel approach to analyze the demand–supply of health care services using taxi data. Taxi data provide observations of individual travel activities, and hence can be used to characterize the actual demand–supply of health care services. We apply the proposed approach in Guangzhou, China to carry out a case study. The results show the spatial disparities in health care service access. About 21.05% of the total population has high hospital accessibility, while the remaining 78.95% has relatively low hospital accessibility (i.e., roughly an “80/20” distribution). It is found that 6.29% of the population lives in high‐density suburban communities but has relatively low hospital accessibility. Most of the hospitals serve a population that is compatible with their capacity. One hospital is found to have a small capacity but to serve a large population, while two hospitals have relatively high capacities but serve small populations. These findings can help improve our understanding of spatial inequalities in public service provision, and may also provide useful information to address the health care problems of an aging population in contemporary, rapidly urbanizing China.     

Change detection assessment using fuzzy sets and remotely sensed data: an application of topographic map revision

This paper explores a methodology for computing the amount of changes that have occurred within an area by using remotely sensed technologies and fuzzy modelling. The discussion concentrates on the formulation of a standard procedure that, using the concept of fuzzy sets and fuzzy logic, can define the likelihood of changes detected from remotely sensed data. Furthermore, an example of how fuzzy visualisation of areas undergoing changes can be incorporated into a decision support system for prioritisation of areas requiring topographic map revision and updating is presented. By adapting the membership function of the fuzzy model to fit the shape of the histogram characterising the change image (derived from any of the common pre-classification methods of change detection), areas can be identified according to their likelihood of having undergone change during the period of observation.     

十进制线性四叉树的点云数据格网索引方法研究

三维激光点云数据是海量三维点的集合,导致数据量庞大,组织和管理困难,不仅增加了系统负荷,而且大大降低了点云数据后续处理效率。该文针对海量点云数据的组织与管理中遇到的加载和显示效率低、建立索引困难、不能实时动态显示等问题,提出了基于十进制线性四叉树的点云数据格网索引方法,该方法用四叉树结构分割点云数据和用SQL Server数据库存储,采用Morton码或矩形区域对点云数据进行分块空间索引,结合空间索引和数据库的优势对点云数据进行高效、动态、智能管理。实验结果表明,该方法较好地解决海量点云数据的组织与管理效率低下,不能实时动态显示的问题。     

Building footprint database improvement for 3D reconstruction: A split and merge approach and its evaluation

We present a general framework to improve a vectorial building footprint database consisting of a set of 2D polygons. The aim of this improvement is to make the database more proper to subsequent 3D building reconstruction at a large scale. Each polygon is split into several simple polygons guided by a digital elevation model (DEM). We say that this segmentation is vectorial as we produce segmentations that intrinsically have simple polygonal shapes, instead of doing a raster segmentation of the DEM within the polygon then trying to simplify it in a vectorization step. The method is based on a Mumford and Shah like energy functional characterizing the quality of the segmentation. We simplify the problem by imposing that the segmentation edges have directions present in the input polygon over which the DEM is defined. We evaluate the validity of the proposed method on a very large dataset and discuss its pros and cons based on this evaluation.     

地名数据生产与建库监理内容与方法研究

地名数据生产与建库监理是提高地名数据质量、保证地名数据库工程建设顺利推进的基本保障。文中主要从质量控制的角度,对地名数据生产准备、生产过程、检查验收、地名入库等阶段的监理内容及其方法措施进行论述,可为地名生产与建库监理的体系建立和具体实施提供参考与借鉴。     

丽水市城市空间基础地理数据库的建设与应用

城市基础数据库是"数字城市"建设的重要组成部分。本文介绍了丽水市城市空间基础地理数据库的建设和应用情况,论述了空间基础地理数据库中数据的更新过程和管理功能。     

利用1:2000数字地形图编制1:10000建库数据的实践与探索

目前,我国很多城市拥有适时更新的大比例尺数字地形图,如何利用已有资源快捷地编制中小比例尺的建库数据,值得研究和探讨.文中给出在MicroStation中利用1:2 000数字地形图编制1:10 000建库数据的方法与技术.使用数字高程模型编制等高线并结合地形和高程模型进行等高线的编辑、裁剪,采取从数字地形图上直接拾取和外业调查相结合的方式获取地形要素的属性值,有效解决编制地理信息系统建库数据的实际问题.     

建库与制图数据一体化生产与管理技术的研究与实践

本文通过对目前建库与制图数据生产与管理的现状,以及这两种数据特点的分析,阐明了建库与制图数据生产与管理一体化的真正含义和一体化空间数据模型的内容,通过在建库与制图数据一体化软件平台--Map-Store基础测绘版上的实践,总结了建库与制图数据一体化工艺方法和质量控制,在建库与制图数据一体化数据管理的基础上实现两种数据快...     

Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut

We present a method for automatic reconstruction of permanent structures, such as walls, floors and ceilings, given a raw point cloud of an indoor scene. The main idea behind our approach is a graph-cut formulation to solve an inside/outside labeling of a space partitioning. We first partition the space in order to align the reconstructed models with permanent structures. The horizontal structures are located through analysis of the vertical point distribution, while vertical wall structures are detected through feature preserving multi-scale line fitting, followed by clustering in a Hough transform space. The final surface is extracted through a graph-cut formulation that trades faithfulness to measurement data for geometric complexity. A series of experiments show watertight surface meshes reconstructed from point clouds measured on multi-level buildings.     

信息库建设中的数据字典标准化生产

在信息库的建设中,对原有数据库的整合改造是保证实现信息共享的关键问题。本文基于《信息库-数据字典规范》,将XML模式应用于数据字典的标准模板,设计了一个标准化、实用化的数据字典采集器,达到规范化、自动化采集数据字典的目的,不仅方便数据库的维护,而且可以利用数据字典对数据库结构作检查,保证各参建单位数据库整合改造结果的一致性和协调性。     

Enhancing data privacy with semantic trajectories: A raster‐based framework for GPS stop/move management

Tracking facilities on smartphones generate enormous amounts of GPS trajectories, which provide new opportunities to study movement patterns and improve transportation planning. Converting GPS trajectories into semantically meaningful trips is attracting increasing research effort with respect to the development of algorithms, frameworks, and software tools. There are, however, few works focused on designing new semantic enrichment functionalities taking privacy into account. This article presents a raster‐based framework which not only detects significant stop locations, segments GPS records into stop/move structures, and brings semantic insights to trips, but also provides possibilities to anonymize users’ movements and sensitive stay/move locations into raster cells/regions so that a multi‐level data sharing structure is achieved for a variety of data sharing purposes.     

The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

Abstract

This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001–2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multi-temporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p=0.01.     

Orbit determination of the SELENE satellites using multi-satellite data types and evaluation of SELENE gravity field models

The SELENE mission, consisting of three separate satellites that use different terrestrial-based tracking systems, presents a unique opportunity to evaluate the contribution of these tracking systems to orbit determination precision. The tracking data consist of four-way Doppler between the main orbiter and one of the two sub-satellites while the former is over the far side, and of same-beam differential VLBI tracking between the two sub-satellites. Laser altimeter data are also used for orbit determination. The contribution to orbit precision of these different data types is investigated through orbit overlap analysis. It is shown that using four-way and VLBI data improves orbit consistency for all satellites involved by reducing peak values in orbit overlap differences that exist when only standard two-way Doppler and range data are used. Including laser altimeter data improves the orbit precision of the SELENE main satellite further, resulting in very smooth total orbit errors at an average level of 18 m. The multi-satellite data have also resulted in improved lunar gravity field models, which are assessed through orbit overlap analysis using Lunar Prospector tracking data. Improvements over a pre-SELENE model are shown to be mostly in the along-track and cross-track directions. Orbit overlap differences are at a level between 13 and 21 m with the SELENE models, depending on whether 1-day data overlaps or 1-day predictions are used.     

A comparative evaluation of IRS-1A LISS-II and landsat MSS images using computer-aided lineament data analysis

Lineament patterns detected from remotely sensed data provide useful information to geoscientists, specially in the study of basement tectonics, groundwater targetting and mineral exploration. Improvements in the spatial resolution of satellite images have resulted in the detection of short and faint lineaments which have hitherto gone unnoticed The IRS-1A LISS-II data offers a significant improvement in spatial resolution as compared to the Landsat MSS. A set of computer programmes developed for analysis of lineaments were used to study the parameters such as lineament frequency, length and density in order to quantify the added information derived using IRS-1A LISS-II images. The incremental contribution of LISS-II images are of the order of 100 per cent for lineament frequency and about 60 per cent for total line kilometers of lineaments detected.