Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Scaling geospatial searches in large spatial databases

Modern geographical databases, which are at the core of geographic information systems (GIS), store a rich set of aspatial attributes in addition to geographic data. Typically, aspatial information comes in textual and numeric format. Retrieving information constrained on spatial and aspatial data from geodatabases provides GIS users the ability to perform more interesting spatial analyses, and for applications to support composite location-aware searches; for example, in a real estate database: “Find the nearest homes for sale to my current location that have backyard and whose prices are between $50,000 and $80,000”. Efficient processing of such queries require combined indexing strategies of multiple types of data. Existing spatial query engines commonly apply a two-filter approach (spatial filter followed by nonspatial filter, or viceversa), which can incur large performance overheads. On the other hand, more recently, the amount of geolocation data has grown rapidly in databases due in part to advances in geolocation technologies (e.g., GPS-enabled smartphones) that allow users to associate location data to objects or events. The latter poses potential data ingestion challenges of large data volumes for practical GIS databases. In this dissertation, we first show how indexing spatial data with R-trees (a typical data pre-processing task) can be scaled in MapReduce—a widely-adopted parallel programming model for data intensive problems. The evaluation of our algorithms in a Hadoop cluster showed close to linear scalability in building R-tree indexes. Subsequently, we develop efficient algorithms for processing spatial queries with aspatial conditions. Novel techniques for simultaneously indexing spatial with textual and numeric data are developed to that end. Experimental evaluations with real-world, large spatial datasets measured query response times within the sub-second range for most cases, and up to a few seconds for a small number of cases, which is reasonable for interactive applications. Overall, the previous results show that the MapReduce parallel model is suitable for indexing tasks in spatial databases, and the adequate combination of spatial and aspatial attribute indexes can attain acceptable response times for interactive spatial queries with constraints on aspatial data.

Spatial patterns of fish communities along two estuarine gradients in southern Florida

In tropical and subtropical estuaries, gradients of primary productivity and salinity are generally invoked to explain patterns in community structure and standing crops of fishes. We documented spatial and temporal patterns in fish community structure and standing crops along salinity and nutrient gradients in two subtropical drainages of Everglades National Park, USA. The Shark River drains into the Gulf of Mexico and experiences diurnal tides carrying relatively nutrient enriched waters, while Taylor River is more hydrologically isolated by the oligohaline Florida Bay and experiences no discernable lunar tides. We hypothesized that the more nutrient enriched system would support higher standing crops of fishes in its mangrove zone. We collected 50 species of fish from January 2000 to April 2004 at six sampling sites spanning fresh to brackish salinities in both the Shark and Taylor River drainages. Contrary to expectations, we observed lower standing crops and density of fishes in the more nutrient rich tidal mangrove forest of the Shark River than in the less nutrient rich mangrove habitats bordering the Taylor River. Tidal mangrove habitats in the Shark River were dominated by salt-tolerant fish and displayed lower species richness than mangrove communities in the Taylor River, which included more freshwater taxa and yielded relatively higher richness. These differences were maintained even after controlling for salinity at the time of sampling. Small-scale topographic relief differs between these two systems, possibly created by tidal action in the Shark River. We propose that this difference in topography limits movement of fishes from upstream marshes into the fringing mangrove forest in the Shark River system, but not the Taylor River system. Understanding the influence of habitat structure, including connectivity, on aquatic communities is important to anticipate effects of construction and operational alternatives associated with restoration of the Everglades ecosystem.

Spatial, geomorphological, and seasonal variability of CDOM in estuaries of the Florida Coastal Everglades

This paper demonstrates the usefulness of fluorescence techniques for long-term monitoring and assessment of the dynamics (sources, transport and fate) of chromophoric dissolved organic matter (CDOM) in highly compartmentalized estuarine regions with non-point water sources. Water samples were collected monthly from a total of 73 sampling stations in the Florida Coastal Everglades (FCE) estuaries during 2001 and 2002. Spatial and seasonal variability of CDOM characteristics were investigated for geomorphologically distinct sub-regions within Florida Bay (FB), the Ten Thousand Islands (TTI), and Whitewater Bay (WWB). These variations were observed in both quantity and quality of CDOM. TOC concentrations in the FCE estuaries were generally higher during the wet season (June–October), reflecting high freshwater loadings from the Everglades in TTI, and a high primary productivity of marine biomass in FB. Fluorescence parameters suggested that the CDOM in FB is mainly of marine/microbial origin, while for TTI and WWB a terrestrial origin from Everglades marsh plants and mangroves was evident. Variations in CDOM quality seemed mainly controlled by tidal exchange/mixing of Everglades freshwater with Florida Shelf waters, tidally controlled releases of CDOM from fringe mangroves, primary productivity of marine vegetation in FB and diagenetic processes such as photodegradation (particularly for WWB). The source and dynamics of CDOM in these subtropical estuaries is complex and found to be influenced by many factors including hydrology, geomorphology, vegetation cover, landuse and biogeochemical processes. Simple, easy to measure, high sample throughput fluorescence parameters for surface waters can add valuable information on CDOM dynamics to long-term water quality studies which can not be obtained from quantitative determinations alone.

Spatial and Temporal Changes in Groundwater Salinity in South Florida

A combination of statistical and interpolation methods and Geographic Information System (GIS) spatial analysis was used to evaluate the spatial and temporal changes in groundwater Cl− concentrations in Collier and Lee Counties (southwestern Florida), and Miami-Dade and Broward Counties (southeastern Florida), since 1985. In southwestern Florida, the average Cl− concentrations in the shallow wells (0–43 m) in Collier and Lee Counties increased from 132 mg L−1 in 1985 to 230 mg L−1 in 2000. The average Cl− concentrations in the deep wells (>43 m) of southwestern Florida increased from 392 mg L−1 in 1985 to 447 mg L−1 in 2000. Results also indicated a positive correlation between the mean sea level and Cl− concentrations and between the mean sea level and groundwater levels for the shallow wells. Concentrations in the Biscayne Aquifer (southeastern Florida) were significantly higher than those of southwestern Florida. The average Cl− concentrations increased from 159 mg L−1 in 1985 to 470 mg L−1 in 2010 for the shallow wells (<33 m) and from 1360 mg L−1 in 1985 to 2050 mg L−1 in 2010 for the deep wells (>33 m). In the Biscayne Aquifer, wells showed a positive or negative correlation between mean sea level and Cl− concentrations according to their location with respect to the saltwater intrusion line. Wells located inland behind canal control structures and west of the saltwater intrusion line showed negative correlation values, whereas wells located east of the saltwater intrusion line showed positive values. Overall, the results indicated that since 1985, there was a potential decline in the available freshwater resources estimated at about 12–17% of the available drinking-quality groundwater of the southeastern study area located in the Biscayne Aquifer.

Spatial and Temporal Variability of Dissolved Organic Matter Quantity and Composition in an Oilgotrophic Subtropical Coastal Wetland

Dissolved organic matter (DOM) is an essential component of the carbon cycle and a critical driver in controlling variety of biogeochemical and ecological processes in wetlands. The quality of this DOM as it relates to composition and reactivity is directly related to its sources and may vary on temporal and spatial scales. However, large scale, long-term studies of DOM dynamics in wetlands are still scarce in the literature. Here we present a multi-year DOM characterization study for monthly surface water samples collected at 14 sampling stations along two transects within the greater Everglades, a subtropical, oligotrophic, coastal freshwater wetland-mangrove-estuarine ecosystem. In an attempt to assess quantitative and qualitative variations of DOM on both spatial and temporal scales, we determined dissolved organic carbon (DOC) values and DOM optical properties, respectively. DOM quality was assessed using, excitation emission matrix (EEM) fluorescence coupled with parallel factor analysis (PARAFAC). Variations of the PARAFAC components abundance and composition were clearly observed on spatial and seasonal scales. Dry versus wet season DOC concentrations were affected by dry-down and re-wetting processes in the freshwater marshes, while DOM compositional features were controlled by soil and higher plant versus periphyton sources respectively. Peat-soil based freshwater marsh sites could be clearly differentiated from marl-soil based sites based on EEM–PARAFAC data. Freshwater marsh DOM was enriched in higher plant and soil-derived humic-like compounds, compared to estuarine sites which were more controlled by algae- and microbial-derived inputs. DOM from fringe mangrove sites could be differentiated between tidally influenced sites and sites exposed to long inundation periods. As such coastal estuarine sites were significantly controlled by hydrology, while DOM dynamics in Florida Bay were seasonally driven by both primary productivity and hydrology. This study exemplifies the application of long term optical properties monitoring as an effective technique to investigate DOM dynamics in aquatic ecosystems. The work presented here also serves as a pre-restoration condition dataset for DOM in the context of the Comprehensive Everglades Restoration Plan (CERP).

Environmental Dynamics of Dissolved Black Carbon in Wetlands

Wetlands are ecosystems commonly characterized by elevated levels of dissolved organic carbon (DOC), and although they cover a surface area less than 2 % worldwide, they are an important carbon source representing an estimated 15 % of global annual DOC flux to the oceans. Because of their unique hydrological characteristics, fire can be an important ecological driver in pulsed wetland systems. Consequently, wetlands may be important sources not only of DOC but also of products derived from biomass burning, such as dissolved black carbon (DBC). However, the biogeochemistry of DBC in wetlands has not been studied in detail. The objective of this study is to determine the environmental dynamics of DBC in different fire-impacted wetlands. An intensive, 2-year spatial and temporal dynamics study of DBC in a coastal wetland, the Everglades (Florida) system, as well as one-time sampling surveys for the other two inland wetlands, Okavango Delta (Botswana) and the Pantanal (Brazil), were reported. Our data reveal that DBC dynamics are strongly coupled with the DOC dynamics regardless of location, season or recent fire history. The statistically significant linear regression between DOC and DBC was applied to estimate DBC fluxes to the coastal zone through two main riverine DOC export routes in the Everglades ecosystem. The presence of significant amounts of DBC in these three fire-impacted ecosystems suggests that sub-tropical wetlands could represent an important continental-ocean carrier of combustion products from biomass burning. The discrimination of DBC molecular structure (i.e. aromaticity) between coastal and terrestrial samples, and between samples collected in wet and dry season, suggests that spatially-significant variation in DBC source strength and/or degree of degradation may also influence DBC dynamics.

Effects of Family, Child, and Teacher Demographics on Prekindergarten Children's Access to and Use of Numeracy and Spatial Materials in the Early Education Setting

Florida’s Voluntary Pre-Kindergarten program (VPK) aims to ensure that all 4-year-olds are prepared to excel in K-12 mathematics. Early numeracy/spatial skills are predictive of success in K–12 mathematics. No research has examined whether VPK classrooms are equipped with the materials necessary to teach numeracy/spatial skill. The Pre-Kindergarten Numeracy and Spatial Environment Survey was created to examine the frequency of access to and use of numeracy/spatial materials in VPK classrooms. The 69-item survey was completed by the lead educator from a sample of 62 pre-kindergarten classrooms in Miami-Dade County. Regression analysis results suggest the location of the pre-kindergarten center, the sex distribution of the children in the classrooms or the number of years of experience that the educator has as a lead teacher along with the extra training courses undertaken by the teachers does not affect the access to or the use of, numeracy and spatial materials in the classrooms.

Preschooler’s Comprehension of Spatial Terms

This study aims to understand individual differences in preschooler’s early comprehension of spatial language. Spatial language is defined as terms describing location, direction, shape, dimension, features, orientation, and quantity (e.g location, shape). Spatial language is considered to be one of the important factors in the development of spatial reasoning in the preschool years (Pruden, Levine, & Huttenlocher, 2011). In recent years, research has shown spatial reasoning is an important predictor of successes in STEM (Science, Technology, Engineering, and Mathematics) fields (e.g. Shea, Lubinski & Benbow, 2001; Wai, Lubinksi &Benbow, 2009). The current study focuses on when children begin to comprehend spatial terms, while previous work has mainly focused on production of spatial language. Identifying when children begin to comprehend spatial terms could lead to a better understanding of how spatial reasoning develops. We use the Intermodal Preferential Looking paradigm (IPLP) to examine three-year-old children’s ability to map spatial terms to visual representations. Fourteen spatial terms were used to test these abilities (e.g. bottom, diamond, longer). For each test trial children were presented with two different stimuli simultaneously on the left and right sides of a television screen. A female voice prompted the child to find the target spatial relation (e.g. “can you find the boy pointing to the bottom of the window”; Figure 1). A Tobii X60 eye-tracker was used to record the child’s eye gaze for each trial. For each child the proportion of looking to the target image divided by their total looking during the trial was calculated; this served as the dependent variable. Proportions above .50 indicated that the child had correctly mapped the spatial term to the target image. Preliminary data shows that the number of words comprehended in the IPLP task is correlated to parental report of the child’s comprehension of spatial terms (r[14]=.500, p<.05).

Preschooler’s Comprehension of Spatial Terms

This study aims to understand individual differences in preschooler’s early comprehension of spatial language. Spatial language is defined as terms describing location, direction, shape, dimension, features, orientation, and quantity (e.g location, shape). Spatial language is considered to be one of the important factors in the development of spatial reasoning in the preschool years (Pruden, Levine, & Huttenlocher, 2011). In recent years, research has shown spatial reasoning is an important predictor of successes in STEM (Science, Technology, Engineering, and Mathematics) fields (e.g. Shea, Lubinski & Benbow, 2001; Wai, Lubinksi &Benbow, 2009). The current study focuses on when children begin to comprehend spatial terms, while previous work has mainly focused on production of spatial language. Identifying when children begin to comprehend spatial terms could lead to a better understanding of how spatial reasoning develops. We use the Intermodal Preferential Looking paradigm (IPLP) to examine three-year-old children’s ability to map spatial terms to visual representations. Fourteen spatial terms were used to test these abilities (e.g. bottom, diamond, longer). For each test trial children were presented with two different stimuli simultaneously on the left and right sides of a television screen. A female voice prompted the child to find the target spatial relation (e.g. “can you find the boy pointing to the bottom of the window”; Figure 1). A Tobii X60 eye-tracker was used to record the child’s eye gaze for each trial. For each child the proportion of looking to the target image divided by their total looking during the trial was calculated; this served as the dependent variable. Proportions above .50 indicated that the child had correctly mapped the spatial term to the target image. Preliminary data shows that the number of words comprehended in the IPLP task is correlated to parental report of the child’s comprehension of spatial terms (r[14]=.500, p<.05).