Free Data Sources Research Articles

Toward carbon neutral cities: A comparative analysis between Sentinel 2 and WorldView 3 satellite image processing for tree carbon stock mapping in Brussels

Because of the high costs associated with data sources, urban policymakers struggle to employ cost-effective remote sensing methods for evaluating trees and their potential contributions to atmospheric Carbon Stock (CS). While free data sources like Copernicus Sentinel satellite data could be explored, there are a few studies illustrating its potential for mapping urban tree C. Here, the Sentinel 2 (S2)-derived Normalized Difference Vegetation Index (NDVI) was used to model CS for street trees in Brussels. In parallel, the WorldView 3 (WV3)-derived NDVI layer was also used for a similar study area to compare the CS mapping outcomes regarding dominant tree species. The accuracy level was around 90 % (R²=0.89, r=0.94, and RMSE= 97 kg) in the case of WV3 data, whereas it was about 60 % (R²=0.60, r=0.79, and RMSE = 189.6 kg), even with a coarse resolution regarding the S2 data. This study also shows the strength and scope of using S2 data over WV3 data, illustrating the convenience in terms of accuracy and cost-effectiveness compared to existing methods. The applied methodology could be utilized to monitor urban trees and predict the level of possible carbon sequestration, even considering a larger city like Brussels with a complex agglomeration. It could be a solid additional support for the authorities of European towns and developing countries, especially in terms of being cost-efficient and readily embraced by users.

Measuring the quality and accessibility of urban greenery using free data sources: A case study in Bratislava, Slovakia

The importance of assessing urban greenery is growing, especially in the context of global warming and the emergence of urban heat islands. The paper proposes a set of measurements to evaluate the quality and accessibility of greenery based on freely available geographic data: Sentinel-2 satellite images, Urban Atlas, and Open Street Map. The quality (or availability) of urban greenery is derived from Sentinel-2 images by using the spectral Forest Index (FI). The proposed Neighborhood Green Quality Indexes (NGQI0/30, NGQI350) evaluate greenery in the near and far surroundings of the spatial unit by averaging the FI values. The Accessibility to Green Space Index (AGSI) measures accessibility to green spaces (GS) based on the number of theoretical functional levels within reach and the average normalised distance to the nearest GS across levels. We also propose to separately assess accessibility to parks with amenities through the Accessibility to Urban Parks Index (AUPI). Such areas attract specific groups of the population, such as elderly people and mothers with children, who are among the most vulnerable during heat waves. The proposed set of measurements can be applied to a square grid and to buildings. A mutual comparison of the proposed measurements in Bratislava, the capital of Slovakia, confirmed that each measurement captures different characteristics of urban greenery. The comparison of FI with two other widely used green quality indicators showed that FI achieves the highest correlation with the land surface temperature during hot summer days. When evaluating the precision of the NGQI0/30 calculation, Sentinel-2-derived FI proved to be a sufficient substitute for high-resolution urban greenery data sources. Since the proposed measurements do not require complex calculations and hard-to-obtain data, they are easily applicable to other cities.

Online Health Information Seeking for Mpox in Endemic and Nonendemic Countries: Google Trends Study.

The recent global outbreak of mpox (monkeypox) has already been declared a public health emergency of international concern by the World Health Organization. Given the health, social, and economic impacts of the COVID-19 pandemic, there is understandable concern and anxiety around the emergence of another infectious disease-especially one about which little is known. We used Google Trends to explore online health information seeking patterns for mpox in endemic and nonendemic countries and investigated the impact of the publication of the first in-country case on internet search volume. Google Trends is a publicly accessible and free data source that aggregates worldwide Google search data. Google search data were used as a surrogate measure of online health information seeking for 178 days between February 18 and August 18, 2022. Searching data were downloaded across this time period for nonendemic countries with the highest case count (United States, Spain, Germany, United Kingdom, and France) and 5 endemic countries (Democratic Republic of Congo, Nigeria, Ghana, Central African Republic, and Cameroon). Joinpoint regression analysis was used to measure changes in searching trends for mpox preceding and following the announcement of the first human case. Online health information seeking significantly increased after the publication of the first case in all the nonendemic countries-United States, Spain, Germany, United Kingdom, and France, as illustrated by significant joinpoint regression models. Joinpoint analysis revealed that models with 3 significant joinpoints were the most appropriate fit for these data, where the first joinpoint represents the initial rise in mpox searching trend, the second joinpoint reflects the start of the decrease in the mpox searching trend, and the third joinpoint represents searching trends' return to searching levels prior to the first case announcement. Although this model was also found in 2 endemic countries (ie, Ghana and Nigeria), it was not found in Central African Republic, Democratic Republic of Congo, or Cameroon. Findings demonstrate a surge in online heath information seeking relating to mpox after the first in-country case was publicized in all the nonendemic countries and in Ghana and Nigeria among the endemic counties. The observed increases in mpox searching levels are characterized by sharp but short-lived periods of searching before steep declines back to levels observed prior to the publication of the first case. These findings emphasize the importance of the provision of accurate, relevant online public health information during disease outbreaks. However, online health information seeking behaviors only occur for a short time period, and the provision of accurate information needs to be timely in relation to the publication of new case-related information.

Distinguishing Abrupt and Gradual Forest Disturbances With MODIS-Based Phenological Anomaly Series.

Capturing forest disturbances over time is increasingly important to determine the ecosystem's capacity to recover as well as aiding a timely response of foresters. With changes due to climate change increasing the frequencies, a better understanding of forest disturbances and their role in historical development is needed to, on the one hand, develop forest management approaches promoting ecosystem resilience and, on the other hand, provide quick and spatially explicit information to foresters. A large, publicly available satellite imagery spanning more than two decades for large areas of the Earth's surface at varying spatial and temporal resolutions represents a vast, free data source for this. The challenge is 2-fold: (1) obtaining reliable information on forest condition and development from satellite data requires not only quantification of forest loss but rather a differentiated assessment of the extent and severity of forest degradation; (2) standardized and efficient processing routines both are needed to bridge the gap between remote-sensing signals and conventional forest condition parameters to enable forest managers for the operational use of the data. Here, we investigated abiotic and biotic disturbances based on a set of ground validated occurrences in various forest areas across Germany to build disturbance response chronologies and examine event-specific patterns. The proposed workflow is based on the R-package “npphen” for non-parametric vegetation phenology reconstruction and anomaly detection using MODIS EVI time series data. Results show the potential to detect distinct disturbance responses in forest ecosystems and reveal event-specific characteristics. Difficulties still exist for the determination of, e.g., scattered wind throw, due to its subpixel resolution, especially in highly fragmented landscapes and small forest patches. However, the demonstrated method shows potential for operational use as a semi-automatic system to augment terrestrial monitoring in the forestry sector. Combining the more robust EVI and the assessment of the phenological series at a pixel-by-pixel level allows for a changing species cover without false classification as forest loss.

Performing indoor PM2.5 prediction with low-cost data and machine learning

PurposeThe paper aims to develop a particle matter (PM2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM2.5 require either time-consuming or expensive equipment, thus making PM2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.Design/methodology/approachThis study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.FindingsThe paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.Originality/valueThis work demonstrates that by combining low-cost sensors and ML, indoor PM2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.

The Trajectories, Trends, and Opportunities for Assessing Urban Ecosystem Services: A Systematic Review of Geospatial Methods

Urban ecosystem services (UES) are indispensable for life. Stakeholders are improvising strategies for a more sustainable provisioning of UES. For this purpose and for identifying orientations towards geospatial data in UES studies, the “bibliometric analysis” technique was deployed. The inclinations facilitate assessments pertaining to spatio-temporal oscillations in the supply–demand equilibrium. The propensities are gaining recognition due to time and cost effectiveness. Besides this, Remote Sensing (RS) in conjunction with Geographic Information System (GIS), enables the conduct of synoptic and robust periodic evaluations. The study analyzes inclinations towards RS in contemporary research (2010–2020) focusing, particularly, on urban ecosystem services. It specifically focuses on methodological frameworks and major sources of remotely sensed data. Therefore, a total of 261 records of research articles were identified and retrieved. Subsequently, 79 articles were selected for further processing and content analysis. It transpired that approximately 30% of the selected publications deployed remotely sensed data for assessment purposes. The majority (96%) of such studies were conducted in economically developed and industrialized countries. However, the researchers from both developed and developing countries prefer open software and free data sources. Besides this, they prefer satellite-based optical sensors over image sensors such as TIR, SAR, or light sensors for acquiring data. The findings formulate that Land Use Land Cover (LULC)-based methodologies and inclinations for assessing regulating services are more frequently pursued. The findings revealed that enhanced research collaborations, access to data, and assessment gadgets are obligatory for capacity building in developing regions. Knowledge sharing and cost-effective access to RS and GIS based platforms are incumbent for ensuring urban environmental sustainability in developing economies.

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow & Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PDF

Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data

Forests are a global environmental priority that need to be monitored frequently and at large scales. Satellite images are a proven useful, free data source for regular global forest monitoring but these images often have missing data in tropical regions due to climate driven persistent cloud cover. Remote sensing and statistical approaches to filling these missing data gaps exist and these can be highly accurate, but any interpolation method results are uncertain and these methods do not provide measures of this uncertainty. We present a new two-step spatial stochastic random forest (SS-RF) method that uses random forest algorithms to construct Beta distributions for interpolating missing data. This method has comparable performance with the traditional remote sensing compositing method, and additionally provides a probability for each interpolated data point. Our results show that the SS-RF method can accurately interpolate missing data and quantify uncertainty and its applicability to the challenge of monitoring forest using free and incomplete satellite imagery data. We propose that there is scope for our SS-RF method to be applied to other big data problems where a measurement of uncertainty is needed in addition to estimates.

Glacier mapping and change analysis in Chandra basin, Western Himalaya, India during 1971–2016

ABSTRACT For carrying out glacier change analysis in Chandra basin, Western Himalaya, India during 1971–2016, glacier inventory is generated for year 2002 using Landsat Enhanced Thematic Mapper+ (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 2 (GDEM V2) data. Total 395 glaciers are mapped covering 703.3 ± 20.4 km2 area with minimum glacier size being >0.02 km2. Out of these, 59 glaciers have debris-covered ice covering an area of 67.2 ± 1.9 km2. In this study, a subset of 169 glaciers is used for glacier area change analysis using Corona KH-4B (1971), Landsat ETM+ (2002) and Sentinel-2 (2016) images due to the availability of cloud free data sources. In 1971, 639.4 ± 5.8 km2 glacier area is estimated which decreased to 620.2 ± 18.0 km2 (–3.0 ± 3.0%) in 2002 and 608.1 ± 10.3 km2 (–4.9 ± 1.9%) in 2016. The change in glacier length of Samudra Tapu and Gepang Gath glaciers is showing increasing trend since last two decades as compared to Hamtah, Chhota Shigri and Bara Shigri glaciers due to presence of pro-glacial lake at their snout. Clean-ice glaciers, glaciers with low mean elevation, glaciers having steep surface, glaciers with west to south and east to south orientation, small size glaciers and glaciers having pro-glacial lake at the snout are responsible to higher loss in glacier area. Statistical trend analysis using Mann-Kendall test and Sen’s slope estimator test show that Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) based annual mean temperature have rising trend at a rate of 0.02°C per year during 1961–2015, and can be ascribed as one of the factors for glacier shrinking.

Multiscale cartographic visualization of harmonized datasets

ABSTRACTWith the product TopPlusOpen, the Federal Agency for Cartography and Geodesy (BKG) has for the first time provided a freely accessible web map, which is based entirely on open or free data sources. For this purpose, the BKG processed and combined many different data sources in order to achieve the best possible map presentation. Among others, free official spatial data of the Federal Government and the Open Data states Berlin, Hamburg, North Rhine-Westphalia and Thuringia are presented in the product. Furthermore, Brandenburg, Mecklenburg-Western Pomerania and Rhineland-Palatinate provide their official spatial data for TopPlusOpen based on a cooperation agreement so that also these states are presented exclusively by official data. The services of the TopPlusOpen were released in September 2017. The product was very well received by the public, private and scientific sectors and is already used as a background map in many geoportals. The free access and the cross-border map presentation are especially mentioned.

A survey of web resources and tools for the study of TCM network pharmacology

BackgroundTraditional Chinese medicine (TCM) treats diseases in a holistic manner, while TCM formulae are multi‐component, multi‐target agents at the molecular level. Thus there are many parallels between the key ideas of TCM pharmacology and network pharmacology. These years, TCM network pharmacology has developed as an interdisciplinary of TCM science and network pharmacology, which studies the mechanism of TCM at the molecular level and in the context of biological networks. It provides a new research paradigm that can use modern biomedical science to interpret the mechanism of TCM, which is promising to accelerate the modernization and internationalization of TCM.ResultsIn this paper we introduce state‐of‐the‐art free data sources, web servers and softwares that can be used in the TCM network pharmacology, including databases of TCM, drug targets and diseases, web servers for the prediction of drug targets, and tools for network and functional analysis.ConclusionsThis review could help experimental pharmacologists make better use of the existing data and methods in their study of TCM.

Estimation of Polder Retention Capacity Based on ASTER, SRTM and LIDAR DEMs: The Case of Majdany Polder (West Poland)

This study compares four digital elevation models (DEMs), based on various data sources, to define polder retention capacities. Two commercial and two publically available, free of charge data sources were used. Commercial sources are DEMs based on aerial images and LIDAR (Light Detection and Ranging) data. Free data source DEMs generated are based on: SRTM (Shuttle Radar Topography Mission) and ASTER GDEM (ASTER Global Digital Elevation Model). In addition, the impact of the spatial resolution of the numerical terrain model on the calculated polder volume was evaluated. A DEM based on LIDAR data was used as the reference model and was supplemented with our own geodetic GPS (Global Positioning System) measurements. In flood modeling and management, including retention of river valleys and polders, it is necessary to properly estimate their capacity and the relation between capacity and water level. The study showed the impact of quantitative and qualitative data sources in determining the retention capacity of a polder.

Complex species distribution models of Goldcrests and Firecrests densities in Poland: are remote sensing‐based predictors sufficient?

AbstractSpecies distribution models should identify ecological requirements of species and predict their spatial density. However, data from remote sensing sources are often used alone as predictors in modelling distributions. Such data will only produce accurate models if features that are distinguishable by remote sensing are a good match to the environmental traits that delineate habitat requirements. Both the Goldcrest Regulus regulus and the Firecrest Regulus ignicapilla respond to complex features of habitats that are not described by simple remote sensing data. We tested the usefulness of remote sensing data as a predictor for two Regulus species according to data from 970 study plots sized 1 × 1 km. Predictors were aggregated using the PCAs and related to the Hayne estimator of species density using GAMs. The models based on both remote sensing data and detailed environmental data proved to be better than the model based only on remote sensing data and/or detailed forest structure data. The Goldcrest reached the highest density in areas with a high share of old spruce‐dominated forests with a substantial share of the fir, avoiding the pine, and it preferred forests with a low number of tree species. In turn, the Firecrest favoured old forests, dominated by the spruce and the beech, with an admixture of single old fir and larch trees, avoiding the pine, and preferring forests with a high number of tree species. We suggest using not only free data sources, but also more detailed data containing thorough information on forest inventory derived from ground measurements.

Mind the Gap: Global Data Sharing

Mind the Gap: Global Data Sharing

Who's Googling What? What Internet Searches Reveal about Hurricane Information Seeking

Two freely available, searchable databases that track the normalized interest in specific search queries, Google Trends and Google Insights, were used to illustrate spatial and temporal patterns in hurricane information seeking. Searches for the word “hurricane” showed a seasonal pattern with spikes in hurricane searches that corresponded to the severity of the storms making landfall. Regional variation in “hurricane” searches was largely driven by the location and magnitude of hurricane landfalls. Catastrophic hurricanes such as Hurricane Katrina captured national attention. A great deal of regional variation in search volume existed prior to Hurricane Ike's landfall. Not as much variation was seen before Hurricane Gustav and Tropical Storm Fay. This variation appeared to be related to changes in the 5-day track forecast as well as other factors such as issuance of watches and warnings. Searches from Louisiana experienced a sharp decrease after the 5-day track forecast shifted away from the state, but before Ike made landfall. Normalized daily visits to Weather Underground during August/September 2008 followed the same pattern as the Google searches. The most popular hurricane-related search terms at the national level prior to landfall dealt with forecast track and evacuation information while searches after landfall included terms related to hurricane damage. There are limitations to using this free data source, but the study has implications for the literature as well as practical applications. This study provides new information about online search behavior before a hurricane that can be utilized by those who provide weather information to the public.

Strategies for Incorporating High-Resolution Google Earth Databases to Guide and Validate Classifications: Understanding Deforestation in Borneo

International climate change mitigation initiatives such as REDD-plus have fuelled the need for forest monitoring efforts that focus especially on the carbon rich natural ecosystems that are found in the humid tropics. Such monitoring efforts must tackle challenges intrinsic to these regions, such as high atmospheric contamination from particulates and persistent cloud cover. The emergence of new high-resolution platforms like Google Earth offers new potential scientific uses that can help meet these challenges. Using data from MODIS and detailed observation of Google Earth images, we have produced a yearly time series of deforestation hotspots for the island of Borneo for the 2000 to 2009 period. Our workflow and results demonstrate how multiple free data sources can be combined to greatly enhance the individual capacities of each. The methodology employed to produce this time series demonstrates simple, low-expense techniques that can be used to circumvent the obstacles that typically hinder systematic remote sensing in Borneo and other heavily clouded areas.

The Internet as Collateral Informant

Back to table of contents Previous article Next article Letters to the EditorFull AccessThe Internet as Collateral InformantGEOFFREY NEIMARK M.D.MATTHEW OWEN HURFORD M.D.JOSEPH DiGIACOMO M.D.,GEOFFREY NEIMARK M.D.Search for more papers by this authorMATTHEW OWEN HURFORD M.D.Search for more papers by this authorJOSEPH DiGIACOMO M.D.Search for more papers by this author,Published Online:1 Oct 2006AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: In this letter, we describe a novel and potentially important way in which the Internet has influenced our practice of clinical psychiatry—the use of the Internet as a collateral informant in the psychiatric encounter.“Mr. J” is a 38-year-old man who came to our crisis response center with suicidal ideation without a plan. Recently, the patient had been kicked out of his girlfriend’s house because of his ongoing abuse of crack cocaine. The resident on-call performed a medical history and physical examination, including a suicide risk assessment, but the patient’s denial of prior suicide attempts led the resident to believe that the patient was at low risk for suicide overall. While writing the patient’s medical history and physical examination results, the resident decided to perform an Internet “Google” search on the patient and discovered a newspaper article from 3 months earlier detailing how Mr. J was pulled by police from a nearby river and admitted to a local psychiatric hospital after he jumped off a major bridge in what was described as a suicide attempt.Internet search engines such as Google process billions of websites in a matter of milliseconds to produce a hierarchical arrangement of “hits” that match the search criteria. Thus, entering an individual’s name into a search engine can reveal interesting results, ranging from newspaper articles to personal Web pages to court cases that are a matter of public record. In the case presented, a single Internet search, performed in a matter of milliseconds, revealed information that would be vital to determining the patient’s ultimate disposition. While Internet-based mental health screening (1) and treatment (2 , 3) applications have been examined, there has been, to our knowledge, no investigation into the role of Internet-accessible personal information in the clinical evaluation of psychiatric patients. Our case illustrates some of the ways in which the Internet can be used to aid decision making in clinical situations. Although the information gleaned from the Internet frequently has, at best, an adjunctive role, this data can be, at times, invaluable in the decision-making process. What are the implications of having these new and powerful, fast, and free data sources at our disposal? Should all of our new patients be “Googled”? Should they be informed that we are indeed “Googling” them? How should we assess the quality and accuracy of Internet data? What about other potentially useful, although perhaps more controversial, sources of information on the Internet? Many states now have websites that list parole absconders and wanted fugitives and offer online registries of sex offenders. In addition, elements of patients’ financial, criminal, and civil histories can be obtained on the Internet. What, if any, role could and should this sort of information have in clinical evaluations, and what are the ethics of this? We hope that this letter is only the beginning of further exploration and discussion of these complicated and exciting new issues involving the role of the Internet in clinical psychiatry.Philadelphia, Pa.Reprints are not available; however, Letters to the Editor can be downloaded at http://ajp.psychiatryonline.org.

The Internet as Collateral Informant

Back to table of contents Previous article Next article Letters to the EditorFull AccessThe Internet as Collateral InformantGEOFFREY NEIMARK M.D.MATTHEW OWEN HURFORD M.D.JOSEPH DiGIACOMO M.D.,GEOFFREY NEIMARK M.D.Search for more papers by this authorMATTHEW OWEN HURFORD M.D.Search for more papers by this authorJOSEPH DiGIACOMO M.D.Search for more papers by this author,Published Online:1 Oct 2006AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail To the Editor: In this letter, we describe a novel and potentially important way in which the Internet has influenced our practice of clinical psychiatry—the use of the Internet as a collateral informant in the psychiatric encounter.“Mr. J” is a 38-year-old man who came to our crisis response center with suicidal ideation without a plan. Recently, the patient had been kicked out of his girlfriend’s house because of his ongoing abuse of crack cocaine. The resident on-call performed a medical history and physical examination, including a suicide risk assessment, but the patient’s denial of prior suicide attempts led the resident to believe that the patient was at low risk for suicide overall. While writing the patient’s medical history and physical examination results, the resident decided to perform an Internet “Google” search on the patient and discovered a newspaper article from 3 months earlier detailing how Mr. J was pulled by police from a nearby river and admitted to a local psychiatric hospital after he jumped off a major bridge in what was described as a suicide attempt.Internet search engines such as Google process billions of websites in a matter of milliseconds to produce a hierarchical arrangement of “hits” that match the search criteria. Thus, entering an individual’s name into a search engine can reveal interesting results, ranging from newspaper articles to personal Web pages to court cases that are a matter of public record. In the case presented, a single Internet search, performed in a matter of milliseconds, revealed information that would be vital to determining the patient’s ultimate disposition. While Internet-based mental health screening (1) and treatment (2 , 3) applications have been examined, there has been, to our knowledge, no investigation into the role of Internet-accessible personal information in the clinical evaluation of psychiatric patients. Our case illustrates some of the ways in which the Internet can be used to aid decision making in clinical situations. Although the information gleaned from the Internet frequently has, at best, an adjunctive role, this data can be, at times, invaluable in the decision-making process. What are the implications of having these new and powerful, fast, and free data sources at our disposal? Should all of our new patients be “Googled”? Should they be informed that we are indeed “Googling” them? How should we assess the quality and accuracy of Internet data? What about other potentially useful, although perhaps more controversial, sources of information on the Internet? Many states now have websites that list parole absconders and wanted fugitives and offer online registries of sex offenders. In addition, elements of patients’ financial, criminal, and civil histories can be obtained on the Internet. What, if any, role could and should this sort of information have in clinical evaluations, and what are the ethics of this? We hope that this letter is only the beginning of further exploration and discussion of these complicated and exciting new issues involving the role of the Internet in clinical psychiatry.Philadelphia, Pa.Reprints are not available; however, Letters to the Editor can be downloaded at http://ajp.psychiatryonline.org.