dataesr/scientific-paragraphs-categorization huggingface dataset:
Extrapolation of Census Data Few countries collect household demographic and socioeconomic statistics more than twice per decade. Previous ridership elasticity studies have used linear extrapolation to estimate monthly values from annual or quinquennial censusbased data sources  (Chiang et al. , 2011; Lee & Lee, 2013). Linear extrapolation provides a more finite amount of information to the research, but also ensures that change in demographic and socioeconomic variables is captured and accounted for in the modelling process. Observations from the  Several datasets were cleaned and adjusted to ensure that all candidate variables closely represented those identified in the literature as being determinantal to transit demand. For example, Section 3. 2. 12 highlights that households with children can be determinantal to transit demand, but the Canadian Census of Population provides separate counts based on the material status of the household.
These datasets were therefore aggregated to provide a single count tabulating all households with children in each census period. Varying Observations were then merged in Microsoft Excel so that values corresponding with each dissemination area could be visualized in the same spreadsheet. The merge was completed using the Geographic Unique Identifier (GEOUID) of the dissemination area. Observations were then extrapolated in Microsoft Excel using the "Trend" function. This process was completed for each Dissemination Area within the study area. Figure  7  illustrates how the process was conducted, and further demonstrates how extrapolated figures accurately reflect changing socioeconomic and demographic conditions compared to the use of time-invariant values. Once the projection was completed, each dissemination area contained a projected monthly estimate for each of the factors obtained from the CHASS server.
Extracting Census Data Using Overlay Analysis Station-level external variable datasets were then identified using overlay analysis in ArcGIS. A spreadsheet containing the projected external variable data was first uploaded to ArcGIS. A shapefile representing the size and extent of all dissemination areas in Ontario, consistent with the 2016 Census, was also uploaded to ArcGIS via Statistics Canada. Figure  8  shows how station catchment boundaries previously delineated in Section 5. 1. 2 were used to identify dissemination areas and corresponding external variable datasets correlated with customer origin location. Overlay analysis was used to ensure that external variable datasets obtained at the stationlevel were representative of proven customer origin locations and the intensity of boardings.
As shown in Figure  9 , only dissemination areas whose centroid was located within the estimated station catchment area boundary were selected. Using the "Calculate Geometry" tool, the total area of all dissemination areas selected was also calculated. A spreadsheet containing the projected datasets within the estimated station catchment boundary and the area of the associated dissemination areas was extracted using the "Export" tool for further processing in Microsoft Excel. !"#$%&'(") +,)-('. !" = ∑ !"#$%&'(") # !" $ #%& ∑ 12,& # " $ #%& ∀ -'&'(")-, % &)5 6")'ℎ-, 8 Eq. 11 +,)-('. "9 :"$-,ℎ"%5-;('ℎ <ℎ(%52,) !" = ∑ :"$-,ℎ"%5-;('ℎ <ℎ(%52,) # !" $ #%& ∑ 12,& # " $ #%& ∀ -'&'(")-, % &)5 6")'ℎ-, 8 Eq. 12 !,2=,)' !"#$%&'(") >,6&%, !" = ? ∑ >,6&%, !"#$%&'(") # !" $ #%& ∑ !"#$%&'(") # !" $ #%& @ A 100∀ -'&'(")-, % &)5 6")'ℎ-, 8 Eq.
Notably, only a single observation for each census tract was provided, and archived data from previous Census' could not be obtained. Therefore, projected values could not be computed. This dataset was uploaded into ArcMap so that data from census tracts located within the previously identified station catchment areas could be downloaded for further analysis. Count data for each census tract was uploaded and linked with a shapefile illustrating all census tract boundaries in southern Ontario. Data was extracted using the same process as outlined in the Section 5. 1. 4. A separate dataset was download and exported for each station in the study area. Each dataset was processed so that a value representing employment density could be estimated. This was done by summing the number of incoming commuters in a given month in a given station area, and normalizing it by the size of all census tracts within the station catchment boundary.
Service Quantity Service data was originally compiled using archived service vetting reports supplied by Metrolinx. However, it was discovered that service quantity was being recorded at the corridor level, using a metric of number of trips per corridor. Unfortunately, this metric ignores the impact of express and local trips on service distribution, as all stations along a corridor were considered to receive the same level of service regardless of service type. Further investigation of internal databases revealed that service data was only recorded using this method, meaning that industry standard metrics, such as vehicle revenue hours were unavailable for analysis. To resolve this problem, it was determined that number of trips per station, rather than corridor level counts, should be used to represent service supply as this metric delineates between stations that have a greater supply of service compared to others.
A Python script was used to extract trip data from archived  GO Transit General Transit Feed  Specification (GTFS) files obtained from "transitfeeds. com"  (Ionescu, 2020). GTFS files were used as they contain archived service schedules, meaning that number of trips per station could be estimated for the duration of the time-series. Individual estimates could have been obtained for each month in the timeseries, however internal Metrolinx service reports indicated that service changed only 17 times over the time-series. Therefore, trip data gathered from a single GTFS file could be assigned to multiple observation periods, as the same level of service was offered over consecutive observation periods. A list of GTFS feeds used in the analysis is outlined in Appendix F. Once processed, trip data at the station level including the arrival time, departure time, direction of travel, and station destination of each vehicle was obtained.
Pivot tables were used to extract station-level trip counts for each trip type. Trips arriving but terminating at a given station were excluded from the count, as no boardings would result from these trips. Unlike the number of boardings dataset, final counts were not normalized by the number of business days in the observation period as data was already obtained at the daily level. Service quantity therefore took the following form: J&%K#7& L:'/0#0. &! = ? =:0B-:/E M%#,3 " ! ∀ -'&'(")-, % &)5 6")'ℎ-, 8 Eq. 19 Where: • ! = a given month, • " = a given station, • # = a given business day, • =:0B-:/E M%#,3 = a trip arriving at a given station and continuing service to other stations along the corridor,.
Feeder Bus Connection Quality Feeder bus connection quality can be recorded by determining the transit travel time from areas that have a concentrated number of riders to the associated access station. Otherwise known as the transit access time, this measure allows service characteristics, such as service quality and service quantity, to influence the travel time estimate. Additionally, estimating the travel time from areas of proven customer origin allows system accessibility to be incorporated into the measure, as access times will increase if minimal or dispersed service is provided within these areas. Transit access times can be calculated using the Network Analyst tool in ArcGIS Pro. The software allows the user to upload a point of customer origin, a GTFS file outlining the extent and quality of transit service in the area, a point of customer destination, and a road network within the study area.
Data Analysis As outlined in Section 5. 1, monthly station-level observations for one dependent variable and 16 independent variables were obtained over a 48-month time-series from January 2016 to December 2019. As noted in the descriptive analysis of the dependent variable, it was theorized that ridership could be influenced by seasonal effects. Therefore, dummy variables indicating the season of observation were also included in the analysis. In total, one dependent and 19 independent variables were compiled in Microsoft Excel for processing. Separate spreadsheets that compiled observations by trip type were further created. A summary of all variables included in the analysis is outlined in Table  7. Summary statistics for the above datasets were then calculated using the "summary" function in R-Studio.
Initial Formatting of Datasets Data was then organized using a long panel data format, where each row represents one time point per entity. Therefore, the entity (station) and time (month and year) were specified in the first two columns, with corresponding dependent / independent variable observations specified in the following columns. Observations were only included if service was offered at a given station during a given time period. Since service is offered at all GO Transit rail stations during a. m. peak period, this resulted in a balanced panel for this model. Other time periods, such as midday off-peak, were not balanced as service was gradually added to some stations throughout the duration of the time series.
Natural Logarithm Transformations All continuous variables were then transformed by their natural logarithm. Log-log transformations are commonly used in ridership elasticity studies for several reasons. First, the coefficients estimated in regression model outputs can be directly interpreted as ridership elasticities, because they represent the percent change in demand when an independent variable is increased by 1%  (Holmgren, 2007; Li et al. , 2020; Stover & Christine Bae, 2011; Taylor et al. , 2009). Secondly, the use of logarithm transformations can normalize the skewness of datasets, which has been shown to increase the fit of models and thereby improve model performance  (Durning & Townsend, 2015; Taylor et al. , 2009). The transformation was completed using the "LM" function in Microsoft Excel.
Data Cleaning A number of observations were found to contain fare price and ridership counts with a value of less than one. This could have been a result of internal processing errors, as employees could have been testing the PRESTO system or associated proof of payment infrastructure, resulting in invalid observations. Since the natural log of any number less than one is a negative number, this prevented the regression analysis from occurring as the software package used to compute the regression analysis is constrained to positive values. Observations containing a value of less than one in either category were therefore removed to prevent this error from occurring. In total, 35 observations were removed from the p. m. peak dataset, 136 observations were removed from the evening off-peak dataset, while no observations were removed from the a.
For this study, a fairly unrestrictive correlation coefficient cut-off of 0. 85 was used to ensure minimal loss of information from the analysis. VIF scores were also used to identify independent variables whose correlation coefficients might be overestimated due to multi-collinearity. A cut-off of 10 was used to identify such variables. The "corrplot" package in R-Studio was used to estimate correlation coefficients for all independent variables in each time period  (Wei & Simko, 2017). VIF scores for each independent variable were also estimated in R-Studio using the "faraway" package  (Faraway, 2016). A full summary of the results is provided in Appendix J. As shown in Table  8 , independent variables with coefficients and VIF scores above the established thresholds are highlighted in red and were removed from each model.
Evaluation of Initial Unrestricted Models Model diagnostic tools were applied to each set of models to determine which estimator best fit the data. First, a Breusch-Pagen Lagrange Multiplier test was applied using the "plmtest" function from the "plm" package to compare the pooled OLS and random effect estimators  (Croissant & Miinno, 2008). For each set of models, the test returned significant results, indicating that the use of a random effect estimator should be further investigated. Second, a Breusch-Pagen Lagrange Multiplier test was applied using the "pFtest" function from the "plm" package to compare pooled OLS and fixed effect estimators  (Croissant & Miinno, 2008). Again, the test returned significant results for each set of models, indicating that the use of pooled OLS is not an efficient estimator for these datasets.
Model Selection Per the Hausman test, a fixed effect estimator was selected, and a stepwise regression procedure was completed for each dataset. However, the results were too degraded to generate informative results and discussion. As the stepwise regression procedure continued, the majority of independent variables included in the unrestricted model were eliminated as they were shown to be insignificant in explaining transit demand, while only a few independent variables remained at the conclusion of the procedure. Additionally, some of the remaining independent variables demonstrated inflated coefficients and counterintuitive signs that did not align with previous estimates identified in the literature. Torres-Reyna (2007)  states that this can occur when the structure of the dataset being analyzed does not align with the mathematical concept of the fixed effect estimator.
Because a fixed effect analysis only considers change over time and does not consider cross-sectional differences, such models are only efficient when independent variable datasets demonstrate considerable temporal variation. Since the external variable datasets used were generated from projections and do not reflect real values, this could have resulted in little fluctuation over the time-series, thus resulting in insignificant outputs. Additionally, some independent variables such as employment density and distance to CBD were excluded from the model altogether as they are time invariant. Eliminating these sources of information could have further skewed model outputs and increased model error, as the reviewed literature indicated that these are significant sources of information that help explain transit demand.
As noted in  Lee et al. (2013) , the use of a fixed effect estimator can be inefficient if time-invariant variables are theorized to be significant in explaining transit demand. The literature further notes that if the researcher has reason to believe that cross-sectional differences have some impact on the dependent variable being analyzed, a random effect estimator should be selected  (Torres-Reyna, 2007). When station-level transit demand is analyzed, cross-sectional differences in external variable datasets such as population and employment density are expected to have an influence on ridership, as catchment areas with more residents and workers inherently results in a larger customer base compared to stations located in sprawled and dispersed environments. A random estimator was therefore selected to produce final modelling outputs as this method allowed transit demand to be explained by both cross-sectional, temporal, and time-invariant factors, therefore increasing the accuracy and explanatory capacity of the models.
Model Diagnostics Each random effect unrestricted model was tested for the presence of heteroskedasticity and serial correlation of error terms. A Durbin-Watson test for serial correlation in panel data models was conducted using the "pdwtest" function, while a Breusch-Pagen test for heteroskedasticity was conducted using the "bptest" function obtained from the "lmtest" package  (Croissant & Miinno, 2008; Zeileis, 2002). Both tests were completed in R-Studio. Significant results were found in all models, therefore indicating the presence of heteroskedasticity and serial correlation. Model outputs produced during the stepwise regression procedure were thereby estimated with White robust standard errors, clustered at the station level, to ensure that estimated coefficients and the level of significance associated with each individual variable included in the analysis was correctly specified  (Guerra & Cervero, 2011; Torres-Reyna, 2007; Zeileis, 2002).
Feeder bus connection quality was also eliminated from the model due to insignificance. These findings are most likely a result of parking spaces being available during the evening off-peak period, thus reducing the importance of feeder bus connections and overall parking supply. Consistent with findings from the p. m. peak model, population and employment density generated comparable coefficients, as elasticities of 4. 1% and 4. 03% were identified. This is most likely attributed to trips generated by the presence of service and retail industries, both of which are open later than typical white-collar businesses. Household income and unemployment rate were also found to generate the largest coefficients in the dataset, as elasticities of 23. 88% and 28. 24% were estimated. Again, these findings suggest that both wealthy and marginalized residents are likely use regional rail services either due to increased travel time valuations or as a means of reducing overall transport costs.
m. peak period compared to the other trip types analyzed. Since datasets with more variation are harder to explain, the lower r-squared value as shown for the a. m. peak ridership model is plausible. The results suggest that ridership, specifically during the a. m. peak period, is heavily influenced by explanatory factors not included in the analysis. Regardless,  Miller et al. (2017)  states that this should be expected when station-level ridership is analyzed. A variety of variables, such as neighbourhood gentrification, the opening / closing of local businesses, construction, and special events such as festivals, concerts, and success of sports teams can randomly influence station-level ridership. However, these occurrences are too descriptive to be included in demand studies of this scale.
Discussion The purpose of this thesis was to identify variables associated with station-level ridership throughout the GO Transit rail network. 12 independent variable datasets were used to develop statistically viable regression models that analyzed transit demand across a 48-month time-series. Results from these models were highlighted in the previous chapter. This thesis also sought to understand how these relationships might differ depending on the trip type examined. Separate regression models which analyzed demand during the a. m. peak, midday offpeak, p. m. peak, and evening off-peak time periods were therefore formulated. The results indicate that some factors are more influential in explaining station-level ridership in some time periods compared to others. The following section contrasts our results with previous studies and theorizes how the identified relationships may have been realized.
Previous demand studies have shown that linear extrapolation is commonly used to project and obtain socioeconomic and demographic values during intercensal periods  (Chiang et al. , 2011; Lee & Lee, 2013) , but this method fails to capture local industrial and economic trends that have a significant impact on unemployment rates  (Weden et al. , 2015). Therefore, it is likely that the unemployment rate dataset used for this study was misspecified. It is recommended that results in relation to this variable be interpreted with caution, as the use of real household employment statistics are needed to further explain the relationship between unemployment rate and transit demand.
Processing Capacity As evidenced in Section 5, extensive work was needed to compile, collect, and estimate various independent variable datasets included in this study. Improved model fit and additional demand elasticities could have been realized if extra time and resources were available to gather and process supplementary datasets. To ensure that this thesis was completed with the time constraints of a two-year master's program, the inclusion of additional factors such as land use mix, education, service quality, and the availability of active transit options was not possible. However, since variables capturing both regional geography, economy, population characteristics, transit system aspects, and those of the surrounding automobile / highway system were included in the analysis, these are arguably the best models that could be specified within the time constraints of the study.
Extrapolation of Socioeconomic and Demographic Datasets A time-series of January 2016 to December 2019 was selected as accurate and specific ridership data was available for analysis. As mentioned in Section 5. 1. 3, the majority of socioeconomic and demographic variable datasets were obtained from extrapolated estimates, as the time-series was located within an intercensal period. While extrapolation is commonly used when monthly values need to be estimated using previous observations, error can be introduced to the model as ridership is therefore explained by predicted values rather than real observations. Further, linear extrapolation could have resulted in the inaccurate specification of some external variable datasets, such as unemployment rate, as socioeconomic factors fluctuate heavily based on localized conditions and economic events.
Discrepancy in Dissemination Area Boundaries The majority of socioeconomic and demographic variable datasets were extracted at the dissemination area scale using overlay analysis. As noted in Section 5. 1. 3, the size and extent of some dissemination areas changed between observation periods. This was the case for approximately 20% of the dissemination areas identified within station catchment boundaries, meaning that only a single data point obtained from 2016 Census products were associated with these entities. As a result, extrapolated values could not be estimated for these entities. This inevitably introduces error into the model, as changing socioeconomic and demographic trends were not captured. However, the effect on model performance was negligible, as the proportion of those included is relatively small compared to those where linear extrapolation methods were used.
Conclusion This study sought to identify factors significantly associated with GO Transit rail ridership. 12 independent variable datasets, including those related to the study area's regional geography, economy, population characteristics, transit system aspects, and the surrounding automobile / highway system were collected across a 48-month time-series. Datasets were separated into a. m. peak, midday off-peak, p. m. peak, and evening off-peak periods to determine if relationships differed depending on the trip type analyzed. A random effect linear panel data estimator was then used to determine the explanatory power of each variable. Despite challenges encountered in terms of data availability and processing capacity, the results revealed that ridership was sensitive to service quantity, population density, fuel price, and station location regardless of trip type examined.
Create a 10km buffer around the station using the "Buffer" tool. Once created, select all postal code polygons that intersect this feature. Use the "Export" tool to extract this dataset, and include it as a separate file in the analysis. This shapefile should therefore contain customer origin data, georeferenced at the postal code scale, only within 10km of the station being analyzed. o If customer origin density is being visualized for the purpose of feeder bus connection quality analysis, also exclude observations located within an 800m buffer of the station. This is done as customer origin density was often concentrated in these areas, therefore limiting the level of analysis that could be completed with respect to feeder bus connections. Follow the same steps as outlined in the above paragraph to eliminate these observations.