Paul A. Zandbergen

Positional Accuracy of Assisted GPS Data from High-Sensitivity GPS-enabled Mobile Phones

Utilizing both Assisted GPS (A-GPS) techniques and new high-sensitivity embedded GPS hardware, mobile phones are now able to achieve positioning in harsh environments such as urban canyons and indoor locations where older embedded GPS chips could not. This paper presents an empirical analysis of the positional accuracy of location data gathered using a high-sensitivity GPS-enabled mobile phone. The performance of the mobile phone is compared to that of regular recreational grade GPS receivers. Availability of valid GPS position fixes on the mobile phones tested was consistently close to 100% both outdoors and indoors. During static outdoor testing, positions provided by the mobile phones revealed a median horizontal error of between 5·0 and 8·5 m, substantially larger than those for regular autonomous GPS units by a factor of 2 to 3. Horizontal errors during static indoor testing were larger compared to outdoors, but the difference in accuracy between mobile phones and regular GPS receivers was reduced. Despite the modest performance of A-GPS on mobile phones, testing under various conditions revealed that very large errors are not very common. The maximum horizontal error during outdoor testing never exceeded 30 metres and during indoor testing never exceeded 100 metres. Combined with the relatively consistent availability of valid GPS position fixes under varying conditions, the current study has confirmed the reliability of A-GPS on mobiles phones as a source of location information for a range of different LBS applications.

A comparison of address point, parcel and street geocoding techniques

The widespread availability of powerful geocoding tools in commercial GIS software and the interest in spatial analysis at the individual level have made address geocoding a widely employed technique in many different fields. The most commonly used approach to geocoding employs a street network data model, in which addresses are placed along a street segment based on a linear interpolation of the location of the street number within an address range. Several alternatives have emerged, including the use of address points and parcels, but these have not received widespread attention in the literature. This paper reviews the foundation of geocoding and presents a framework for evaluating geocoding quality based on completeness, positional accuracy and repeatability. Geocoding quality was compared using three address data models: address points, parcels and street networks. The empirical evaluation employed a variety of different address databases for three different Counties in Florida. Results indicate that address point geocoding produces geocoding match rates similar to those observed for street network geocoding. Parcel geocoding generally produces much lower match rates, in particular for commercial and multi-family residential addresses. Variability in geocoding match rates between address databases and between geographic areas is substantial, reinforcing the need to strengthen the development of standards for address reference data and improved address data entry validation procedures.

Positional Accuracy of Spatial Data: Non-Normal Distributions and a Critique of the National Standard for Spatial Data Accuracy

Spatial data quality is a paramount concern in all GIS applications. Existing spatial data accuracy standards, including the National Standard for Spatial Data Accuracy (NSSDA) used in the United States, commonly assume the positional error of spatial data is normally distributed. This research has characterized the distribution of the positional error in four types of spatial data: GPS locations, street geocoding, TIGER roads, and LIDAR elevation data. The positional error in GPS locations can be approximated with a Rayleigh distribution, the positional error in street geocoding and TIGER roads can be approximated with a log-normal distribution, and the positional error in LIDAR elevation data can be approximated with a normal distribution of the original vertical error values after removal of a small number of outliers. For all four data types considered, however, these solutions are only approximations, and some evidence of non-stationary behavior resulting in lack of normality was observed in all four datasets. Monte-Carlo simulation of the robustness of accuracy statistics revealed that the conventional 100% Root Mean Square Error (RMSE) statistic is not reliable for non-normal distributions. Some degree of data trimming is recommended through the use of 90% and 95% RMSE statistics. Percentiles, however, are not very robust as single positional accuracy statistics. The non-normal distribution of positional errors in spatial data has implications for spatial data accuracy standards and error propagation modeling. Specific recommendations are formulated for revisions of the NSSDA.

Residential Proximity to Schools and Daycares An Empirical Analysis of Sex Offense Recidivism

Residential restrictions for sex offenders have become increasingly popular, despite the lack of empirical data suggesting that offenders’ proximity to schools or daycares contributes to recidivism. Using a matched sample of recidivists and nonrecidivists from Florida (n = 330) for the period from 2004 through 2006, the authors investigated whether sex offenders who lived closer to schools or daycares were more likely to reoffend sexually against children than those who lived farther away. No significant differences were found between the distances that recidivists and nonrecidivists lived from schools and daycares. There was no significant relationship between reoffending and proximity to schools or daycares. The results indicate that proximity to schools and daycares, with other risk factors being comparable, does not appear to contribute to sexual recidivism. These data do not support the widespread enactment of residential restrictions for sexual offenders.

Comparison of Dasymetric Mapping Techniques for Small-Area Population Estimates

Dasymetric mapping techniques can be employed to estimate population characteristics of small areas that do not correspond to census enumeration areas. Land cover has been the most widely used source of ancillary data in dasymetric mapping. The current research examines the performance of alternative sources of ancillary data, including imperviousness, road networks, and nighttime lights. Nationally available datasets were used in the analysis to allow for replicability. The performance of the techniques used to examine these sources was compared to areal weighting and traditional land cover techniques. Four states were used in the analysis, representing a range of different geographic regions: Connecticut, New Mexico, Oregon, and South Carolina. Ancillary data sources were used to estimate census block group population counts using census tracts as source zones, and the results were compared to the known block group population counts. Results indicate that the performance of dasymetric methods varies substantially among study areas, and no single technique consistently outperforms all others. The three best techniques are imperviousness with values greater than 75 percent removed, imperviousness with values greater than 60 percent removed, and land cover. Total imperviousness and roads perform slightly worse, with nighttime lights performing the worst compared to all other ancillary data types. All techniques performed better than areal weighting.

Kernel density estimation and hotspot mapping: examining the influence of interpolation method, grid cell size, and bandwidth on crime forecasting

Purpose – The purpose of this paper is to examine the effects of user-defined parameters settings (e.g. interpolation method, grid cell size, and bandwidth) on the predictive accuracy of crime hotspot maps produced from kernel density estimation (KDE). Design/methodology/approach – The influence of variations in parameter settings on prospective KDE maps is examined across two types of interpersonal violence (e.g. aggravated assault and robbery) and two types of property crime (e.g. commercial burglary and motor vehicle theft). Findings – Results show that interpolation method has a considerable effect on predictive accuracy, grid cell size has little to no effect, and bandwidth as some effect. Originality/value – The current study advances the knowledge and understanding of prospective hotspot crime mapping as it answers the calls by Chainey et al. (2008) and others to further investigate the methods used to predict crime.

Positional accuracy of the Wide Area Augmentation System in consumer-grade GPS units

Global Positioning System devices are increasingly being used for data collection in many fields. Consumer-grade GPS units without differential correction have a published horizontal positional accuracy of approximately 10-15m (average positional accuracy). An attractive option for differential correction for these GPS units is the Wide Area Augmentation System (WAAS). Most consumer-grade GPS units on the market are WAAS capable. According to the Federal Aviation Authority (FAA), the WAAS broadcast message provides integrity information about the GPS signal as well as accuracy improvements, which are reported to improve accuracy to 3-5m. Limited empirical evidence has been published on the accuracy of WAAS-enabled GPS compared to autonomous GPS. An empirical study was conducted comparing the horizontal and vertical accuracy of WAAS-corrected GPS and autonomous GPS under ideal conditions using consumer-grade receivers. Data were collected for 30-min time spans over accurately surveyed control points. Metrics of median, 68th and 95th percentile, Root Mean Squared Error (RMSE), and average positional accuracy in the horizontal and vertical dimensions were computed and statistically compared. No statistically significant difference was found between WAAS and autonomous position fixes when using two different consumer-grade units. When using WAAS, a third unit type exhibited a statistically significant improvement in positional accuracy. Analysis of data collected for a 27-h time span indicates that while WAAS is altering the estimated position of a point compared to an autonomous position estimate, WAAS augmentation actually appears to decrease the positional accuracy.

Error propagation models to examine the effects of geocoding quality on spatial analysis of individual-level datasets

The quality of geocoding has received substantial attention in recent years. A synthesis of published studies shows that the positional errors of street geocoding are somewhat unique relative to those of other types of spatial data: (1) the magnitude of error varies strongly across urban-rural gradients; (2) the direction of error is not uniform, but strongly associated with the properties of local street segments; (3) the distribution of errors does not follow a normal distribution, but is highly skewed and characterized by a substantial number of very large error values; and (4) the magnitude of error is spatially autocorrelated and is related to properties of the reference data. This makes it difficult to employ analytic approaches or Monte Carlo simulations for error propagation modeling because these rely on generalized statistical characteristics. The current paper describes an alternative empirical approach to error propagation modeling for geocoded data and illustrates its implementation using three different case-studies of geocoded individual-level datasets. The first case-study consists of determining the land cover categories associated with geocoded addresses using a point-in-raster overlay. The second case-study consists of a local hotspot characterization using kernel density analysis of geocoded addresses. The third case-study consists of a spatial data aggregation using enumeration areas of varying spatial resolution. For each case-study a high quality reference scenario based on address points forms the basis for the analysis, which is then compared to the result of various street geocoding techniques. Results show that the unique nature of the positional error of street geocoding introduces substantial noise in the result of spatial analysis, including a substantial amount of bias for some analysis scenarios. This confirms findings from earlier studies, but expands these to a wider range of analytical techniques.

Influence of street reference data on geocoding quality

Repeatability of street geocoding was characterized in terms of completeness and positional accuracy by using different street network datasets to geocode the same address input file. Match rates were highest for local street centrelines followed by StreetMap USA 2005 and TIGER 2000 data. Positional accuracy was highest for local street centrelines, while StreetMap USA 2005 and TIGER 2000 were nearly identical. Rural addresses were geocoded less accurately than urban addresses. Multi-family residential and commercial, institutional or industrial addresses were geocoded less accurately than urban single family residential addresses. The enhancement of TIGER 2000 data by commercial firms resulted in higher match rates but not in improved positional accuracy. The study has also highlighted the unique nature of multi-family and non-residential addresses in terms of the quality of their street geocoded locations. When such addresses are of specific interest alternatives to traditional street geocoding may need to be considered.

Ensuring Confidentiality of Geocoded Health Data: Assessing Geographic Masking Strategies for Individual-Level Data.

Public health datasets increasingly use geographic identifiers such as an individuals address. Geocoding these addresses often provides new insights since it becomes possible to examine spatial patterns and associations. Address information is typically considered confidential and is therefore not released or shared with others. Publishing maps with the locations of individuals, however, may also breach confidentiality since addresses and associated identities can be discovered through reverse geocoding. One commonly used technique to protect confidentiality when releasing individual-level geocoded data is geographic masking. This typically consists of applying a certain amount of random perturbation in a systematic manner to reduce the risk of reidentification. A number of geographic masking techniques have been developed as well as methods to quantity the risk of reidentification associated with a particular masking method. This paper presents a review of the current state-of-the-art in geographic masking, summarizing the various methods and their strengths and weaknesses. Despite recent progress, no universally accepted or endorsed geographic masking technique has emerged. Researchers on the other hand are publishing maps using geographic masking of confidential locations. Any researcher publishing such maps is advised to become familiar with the different masking techniques available and their associated reidentification risks.