Smartphone App Allows Users to Navigate Maps, Capture and Report Data, and Perform GIS Analyses
Esri has expanded its mobile geographic information system (GIS) platform to now support the iPhone, iPad, and iPod touch. The new ArcGIS for iOS includes both a free downloadable application from Apple’s app store and an API for developers to build custom mapping solutions.
ArcGIS for iOS uses capabilities from ArcGIS Online to provide a seamless and easy user experience. Users can navigate maps and discover assets from Web services as well as capture new data from the field and send information back to the server. This allows ArcGIS Server customers to leverage their GIS investment by using the device to access their corporate data. ArcGIS for iOS also promotes collaboration and information sharing between multiple users in the field.
The ArcGIS for iOS application includes
- Map navigation using native iOS gestures
- Search, Identify, and Measure tools
- Ability to share maps and GIS information with other iOS users
A native application, ArcGIS for iOS serves as a mobile gateway into the ArcGIS system. It provides access to services available through ArcGIS Online or on-site ArcGIS Server deployments hosting authoritative corporate data. The ArcGIS for iOS application consumes maps authored and hosted through ArcGIS Server.
The ArcGIS API for iOS is designed to work with and use Web services available from ArcGIS Server and ArcGIS Online. Developers and Esri partners will use the API to create applications for both external and internal use. They can also easily build applications that work with their own published Web services. The ArcGIS API for iOS is currently in public beta, with final release expected in August 2010.
To learn more, visit www.esri.com/arcgisforios. Outside the United States, contact your local Esri distributor. For a current distributor list, visit www.esri.com/distributors.
[Source: ESRI press release]
National Research Institute of Police Science, Japan
George Kikuchi, Mamoru Amemiya, Tomonori Saito, Takahito Shimada, and Yutaka Harada
“Recent research in the U.S., Europe, and Australia has consistently identified that the risk of victimization is temporally elevated in areas where crimes have occurred in the recent past. The phenomenon has been termed near repeat victimization. While near repeat victimization has been extensively studied for burglaries, the pattern has also been found for violent offenses such as shooting.
“Analysis of near repeat victimization, however, has caught limited attention among criminologists in Japan, where crime rates are drastically lower than in most western countries. It is quite possible that the volumes of crime may need to be sufficiently large for statistically significant near repeat victimization patterns to occur. On the other hand, if near repeat victimization can be identified in low crime nations such as Japan, such results can be insightful from both theoretical and practical viewpoints. From a practical viewpoint, in particular, the extents of spatial and temporal ranges to which near repeat victimizations are likely to occur can be useful for predictive and focused policing.
“The present study conducted a spatio-temporal analysis of crimes using data on crimes reported to the police in order to identify near repeat victimizations across five crime types (violent offense, purse-snatching, theft from vehicles, business burglary, and residential burglary) in Japan. Crime types were disaggregated in order to identify spatial and temporal ranges of near repeat victimizations which were expected to be crime specific.
“The results have confirmed the risk of near repeat victimization for all crime types, except for violent offenses. The statistically significant results for all property offenses are suggestive in terms of theoretical explanations of near repeat victimization. The paper concludes with a discussion of implications of the findings for both criminological theories and crime prevention activities by the police.”
View the presentation [PDF]
The GIS 20: Essential Skills by Gina Clemmer is an indispensable workbook that helps readers master the top 20 skills that are necessary to become proficient in using ArcGIS software. The book is a direct result of a survey that Clemmer recently conducted with geographic information system (GIS) professionals to quantify the primary skills that are needed to be a successful GIS user.
Each of the 20 chapters covers a specific topic related to the essential GIS skills Clemmer determined from the survey, including creating map layouts, preparing data, joining data to maps, working with attribute tables, mapping addresses, querying location, and publishing maps. The book also includes a data CD for completing the exercises.
“The purpose of the book is to provide a focused approach to learning GIS by offering clear, easy-to-follow exercises for the most commonly used GIS skills in the industry today,” says Clemmer. “It is written for professionals with no time for classroom training and can be used for independent study or an as-needed reference.”
Clemmer is president of New Urban Research, Inc., a research and training company in Portland, Oregon. The author, who holds a master’s degree in urban planning from the University of Iowa, has spent the better part of the last decade training thousands of new GIS users across the country. More than 15,000 working professionals have attended the company’s most popular workshop, Mapping Your Community: An Introduction to GIS and Community Analysis.
The GIS 20: Essential Skills (ISBN: 9781589482562, 155 pages, $39.95) is available at online retailers worldwide, at www.esri.com/esripress, or by calling 1-800-447-9778. Outside the United States, visit www.esri.com/esripressorders for complete ordering options, or visit www.esri.com/distributors to contact your local Esri distributor. Interested retailers can contact Esri Press book distributor Ingram Publisher Services.
[Source: ESRI press release]
Nutrition & Food Science, Volume 39 Issue 1, 2009, pages 59 – 69
P.B. Brevard and K.R. Brevard
“Purpose – The purpose of this study is to explore relationships between cardiovascular disease (CVD), cancer (CA), and diet using Geographic Information Systems (GIS) mapping techniques to investigate spatial trends.
“Design/methodology/approach – Databases containing CVD and CA deaths are listed by state in the USA; databases containing state food consumption statistics, therefore, were sought. Available databases indicating dietary patterns were used to create spatial maps of the USA using ArcGIS (ESRI, Redlands, CA, version 9.2), to visually show trends in relationships among CVD, CA, and diet. Correlations and linear regression were used to determine statistical relationships among variables.
“Findings – Maps show visual relationships between CVD and CA death rates, and a statistically significant positive correlation (r=0.765; p=0.0005) was also found. Southeastern states have the highest death rates for both diseases. Negative correlations were found between CVD and CA deaths and household expenditure for nuts (r=-0.525; p=0.0005 and r=-0.526; p=0.0005, respectively), and CVD deaths and fruit and vegetable intake (r=-0.423; p=0.002). Household expenditure for nuts was a predictor of CVD (ß=-0.469, p=0.002) and CA (ß=-0.490, p=0.002) deaths.
“Originality/value – These trends indicate a need for further research on diet and these diseases, and for state-wide dietary studies to facilitate research using GIS mapping. Food consumption patterns, especially nuts, may be related to CVD and CA death rates. Southeastern states should be targeted for nutrition intervention and education programs.”
Scottish Geographical Journal, 1751-665X, Volume 126, Issue 2, 2010, Pages 76 – 100
Jungyul Sohn and Gerrit Knaap
“This study attempts to examine whether new urban housing development has been effectively constrained within the Priority Funding Area (PFA) in Maryland, USA. More specifically, it adopts a propensity score approach to the analysis of panel data on housing starts in Maryland between 1998 and 2003, extracted from the MdProperty View Database. While many other relevant studies use features of local housing markets as the indicators of programme success, this empirical analysis can directly examine the number and the location of newly constructed houses for testing the efficacy of the programme thanks to the detailed information available from the Property View dataset. In order to avoid the endogeneity problem associated with designating PFA, probability of being PFA for each census tract is estimated using a probit model and is included in the panel regression model. The findings of the study suggest that residential parcel development continues to expand at the outside of the PFA in the Smart Growth era.”
Arid Land Research and Management, Volume 22, Issue 2 April 2008, pages 159-177
Thomas Panagopoulos and Maria Dulce Carlos Antunes
“This research integrates the Revised Universal Soil Loss Equation (RUSLE) with geostatistical techniques and a Geographic Information System (GIS) to model erosion potential for soil conservation planning in Quercus suber agrosilvopastoral woodlands in the Algarve region of southern Portugal. Graphical interpretation of the RUSLE parameters was performed using ordinary kriging. Semi-variograms were produced for each parameter. The maps resulting from the interpolation techniques were introduced in a GIS and their values reclassified. After that, spatial modelling was used to develop the final overlay map from all the information of the analyzed soil properties and RUSLE parameters, simulating “a potential soil erosion map.” Hydraulic conductivity and the soil erodibility K factor with a nugget-to-sill ratio of 57% and 67%, respectively, showed the weakest spatial dependence, whereas organic matter demonstrated the strongest (31%). The maps created demonstrate the existence of a heavily textured area in the southern part of the site that could affect erosion and vegetation management techniques. Hydraulic conductivity was higher than 6 cm/h in the northeastern part of the experimental area. The correlation between the spatially interpolated and observed values during the semi-variogram cross-validation, using the data set for method development, was high (r2 > 0.81). The northwestern area was the most adequate for annual fodder cultivation. The most degraded and less suitable areas were in the southern part, with 108 t/ha potential erosion. Site-specific management methods could improve productivity and decrease the risk of erosion. The present research shows that geostatistics and GIS are useful tools for sustainable management of extensive agrosilvopastoral areas.”
Transactions in GIS, Volume 14 Issue 3, June 2010, p 265-282)
Emma JS Ferranti, J Duncan Whyatt, Roger J Timmis, and Gemma Davies
“A method is presented for conditional analysis of spatial and temporal (1961–2007) variations in rainfall under different synoptic situations and different geographic sub-regions, using Cumbria in NW England as a study area. A daily synoptic typing scheme, the Lamb Weather Catalogue, was applied to identify rainfall under three different weather types: south-westerly (SW), westerly (W) and cyclonic (C). Topographic descriptors developed using GIS were used to classify rain gauges into six geographic sub-regions: coastal, windward-lowland, windward-upland, leeward-upland, leeward-lowland, secondary upland. Examining temporal rainfall trends associated with different weather types, in different geographic sub-regions, reveals useful information on changes in rainfall processes. The total rainfall under SW and W weather types is increasing, particularly in upland regions. The increase in SW rainfall is driven by more frequent wet-days, whereas the increase in W rainfall is driven by increases in both wet-day frequency and yield per wet-day. The rainfall under C weather types is decreasing. Combining GIS and synoptic climatology gives insights into rainfall processes under a changing climate. The conditional analysis method can be applied at both local and regional scales, and its success is largely due to the ability of GIS to integrate, visualise, and efficiently model spatial data.”