updated links for report

README.md

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 The CDO Data Science team developed a geographic information system, Potential Accessibility Software Service (PASS). PASS offers an advanced quantitative approach to measure how spatially accessible population demand is to a given service.
 
-Spatial accessibility is the consideration of how physical and social space and place affect how a population can traverse through it to access given service locations (i.e., points of interest, POIs). Though abstract in nature, it can be measured through considerations like where potential population demand is located, the geographic distance to get from the population location to the service locations offered, the supply at the service locations, as well as the probability of a population going to one service location over another based on the capacity. PASS uses the enhanced 3-Step Floating Catchment Area (3SFCA) methodology to accomplish this, which is further explained [in a report, which should be downloaded and viewed in your web browser](docs/pass_method.html).
+Spatial accessibility is the consideration of how physical and social space and place affect how a population can traverse through it to access given service locations (i.e., points of interest, POIs). Though abstract in nature, it can be measured through considerations like where potential population demand is located, the geographic distance to get from the population location to the service locations offered, the supply at the service locations, as well as the probability of a population going to one service location over another based on the capacity. PASS uses the enhanced 3-Step Floating Catchment Area (3SFCA) methodology to accomplish this, which is further explained [in a report, which should be downloaded and viewed in your web browser](https://dsd-esdc-edsc.github.io/pass/).
 
 PASS lets you select a geographic area of interest by panning and zooming on the interactive map, and lets you define the parameters to model spatial accessibility to better reflect Canada's diverse society. For example, individuals living in urban areas versus rural areas, have different assumptions (e.g., willingness to commute further distances) and considerations (e.g., transportation) for how to access a service.
 

docs/pass_config.md

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 # Database Initialization and Population
 
-To populate the database with the necessary data for PASS to run, you will need to execute the `InitSchema` Python class before starting the PASS web app. **It is highly recommended to read the [PASS report that details the methodology to measure spatial accessibility](./pass_method.html), specifically the 'Floating Catchment Area (FCA) Methods: 2SFCA and Enhanced 3SFCA Models' and 'Data' sections, to better understand why the following data is necessary for PASS to operate. 
+To populate the database with the necessary data for PASS to run, you will need to execute the `InitSchema` Python class before starting the PASS web app. **It is highly recommended to read the [PASS report that details the methodology to measure spatial accessibility](https://dsd-esdc-edsc.github.io/pass/), specifically the 'Floating Catchment Area (FCA) Methods: 2SFCA and Enhanced 3SFCA Models' and 'Data' sections, to better understand why the following data is necessary for PASS to operate. 
 
 The `InitSchema.py` file runs several different modules to read, process and store the necessary data for PASS. The image below demonstrates this. 
 
@@ -54,8 +54,8 @@ The `FILES` object specifies the files necessary to read, process and store data
     - `LATITIUDE`: The column that represents the latitude of the POI location.
     - `LONGITUDE`: The column that represents the longitude of the POI location.
     - `INFO_*`: You can add as many different POI location information values are you like. Create a new key name, and then make sure to specify the `NAME`, `TYPE`, `UNIT`, and `DESC`. **`DESC` needs to stay as "info"** so that `InitSchema` can interpret the column as POI information, which can then appear on the map as a popup when a user clicks the POI point locations.
-    - `SUPPLY_*`: You can add as many different POI location supply counts as you like. Create a new key name, and then make sure to specify the `NAME`, `TYPE`, `UNIT`, and `DESC`. **`DESC` needs to stay as "supply"** so that `InitSchema` can interpret the column as a POI supply count, which can then allow the user to select different POI supply counts to model. Refer to the [PASS report](./pass_method.html) to better understand how and why supply counts are used to model spatial accessibility.
-    - `CAPACITY_*`: You can add as many different POI capacity values as you like. Create a new key name, and then make sure to specify the `NAME`, `TYPE`, `UNIT`, and `DESC`. **`DESC` needs to stay as "capacity"** so that `InitSchema` can interpret the column as a POI capacity value, which can then allow the user to select different POI capacity values to model. Refer to the [PASS report](./pass_method.html) to better understand how and why POI capacity values are used to model spatial accessibility.
+    - `SUPPLY_*`: You can add as many different POI location supply counts as you like. Create a new key name, and then make sure to specify the `NAME`, `TYPE`, `UNIT`, and `DESC`. **`DESC` needs to stay as "supply"** so that `InitSchema` can interpret the column as a POI supply count, which can then allow the user to select different POI supply counts to model. Refer to the [PASS report](https://dsd-esdc-edsc.github.io/pass/) to better understand how and why supply counts are used to model spatial accessibility.
+    - `CAPACITY_*`: You can add as many different POI capacity values as you like. Create a new key name, and then make sure to specify the `NAME`, `TYPE`, `UNIT`, and `DESC`. **`DESC` needs to stay as "capacity"** so that `InitSchema` can interpret the column as a POI capacity value, which can then allow the user to select different POI capacity values to model. Refer to the [PASS report](https://dsd-esdc-edsc.github.io/pass/) to better understand how and why POI capacity values are used to model spatial accessibility.
 
 There is an *optional* object `DEMAND_GEO_WEIGHT` that you can also include to create population weighted centroid, that is the centroid is weighted based on populations at a more granular geographic unit (AKA mean-center). This is particularly useful if you are using Census geographic boundaries, so for example, to have Dissemination Area centroids weighed by Dissemination Block boundaries' populations.
 

docs/pass_distance_matrix_api.md

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 # Distance Matrix Calculation Set Up Instructions
 
-The following document provides detailed instructions for how to set up an application programming interface (API) to calculate distance matrixes for car. **It is highly recommended to read the [PASS report that details the methodology to measure spatial accessibility](./pass_method.html), specifically the 'Floating Catchment Area (FCA) Methods: 2SFCA and Enhanced 3SFCA Models' and 'Data' sections, to better understand why a distance matrix is necessary for PASS to operate**.
+The following document provides detailed instructions for how to set up an application programming interface (API) to calculate distance matrixes for car. **It is highly recommended to read the [PASS report that details the methodology to measure spatial accessibility](https://dsd-esdc-edsc.github.io/pass/), specifically the 'Floating Catchment Area (FCA) Methods: 2SFCA and Enhanced 3SFCA Models' and 'Data' sections, to better understand why a distance matrix is necessary for PASS to operate**.
 
 Currently PASS only depends and can use a distance matrix calculated by cars as a mode of transportation. Thus, in order for `InitSchema.py` to create the required `distance_matrix_car` database table, through the use of the `DistanceMatrix` class, a local [OpenRouteService (ORS)](https://github.com/GIScience/openrouteservice) API connection needs to be established and then provided within `config.json`.