style: Opinionated changes to text. Please review

docs/explanation/what_is_tp/index.qmd

@@ -7,27 +7,27 @@ toc: true
 date-format: iso
 ---
 
-Transport Performance (TP) is a metric originally developed by the European Commission in their [2020 work on low carbon urban transport accessibility][euro-commission-paper]. TP puts the population at the centre of its definition by measuring how efficiently a transport network moves the surrounding population to a destination within a certain time frame. A TP value of 100% would mean all the nearby population can travel to a location within the time threshold.
+Transport Performance (TP) is a metric originally developed by the European Commission in their [2020 work on low carbon urban transport accessibility][euro-commission-paper]. TP puts the population at the centre of its definition, by measuring how efficiently a transport network moves the surrounding population to a destination within a certain time frame. A TP value of 100% would mean all the nearby population can travel to a location within the time threshold.
 
 Since TP is bound by a time frame, it is highly dependent on transport modalities; for example, public transit, private vehicle, cycling, and walking. The example discussed on this page considers the public transit network.
 
-It is also dependent on the surrounding population and the destination itself, making TP highly variable across an area. For this reason, it is calculated on a granular scale to build up the TP picture across an area of interest. The example discussed on this page uses populated 200x200m cells.
+TP is also dependent on the surrounding population and the destination itself, making it highly variable across an area. For this reason, it is calculated on a granular scale to build up the TP picture across an area of interest. The example discussed on this page uses populated 200x200m cells.
 
 @fig-tp-definition illustrates how TP is calculated for one cell in the centre of Newport, Wales using a 45 minutes time threshold, an 11.25Km distance limit on the surrounding population, and the public transit network.
 
 ::: {.callout-tip}
 
-`transport_performance` is highly configurable. It caters for different modalities and time/distance thresholds (and more!) beyond the configuration presented on this page. See the [tutorial](../../tutorials/index.qmd) and [API reference](../../reference/index.qmd) pages for more details.
+`transport_performance` is highly configurable. It caters for different modalities and time/distance thresholds (and more!) beyond the configuration presented on this page. See the [tutorials](../../tutorials/index.qmd) and [API reference](../../reference/index.qmd) for more details.
 
 :::
 
 ::: {#fig-tp-definition layout-ncol=2}
 
-![Accessible population - total population that can travel to a cell in central Newport, Wales within 45 minutes by public transit](accessible_pop.PNG){#fig-access}
+![Accessible population - the total population that can travel to a cell in central Newport, Wales within 45 minutes by public transit](accessible_pop.PNG){#fig-access}
 
-![Proximity population - total population surrounding a cell in central Newport, Wales within the distance limit (11.25km)](proximity_pop.PNG){#fig-proxi}
+![Proximity population - the total nearby population to a cell in central Newport, Wales within the distance limit (11.25km)](proximity_pop.PNG){#fig-proxi}
 
-Accessible and proximity population definitions using a 200x200m cells and an example destination in the middle of Newport, Wales.<br><span class="figure-source">Source: ONS Data Science Campus, April 2024.</span>
+Accessible and proximity population definitions using    200x200m cells and an example destination in the middle of Newport, Wales.<br><span class="figure-source">Source: ONS Data Science Campus, April 2024.</span>
 :::
 
 @fig-tp-definition uses a green marker to denote the destination cell and a red dashed line to illustrate the boundary of the nearby population. The dark pink region in @fig-access represents the **accessible population**. This is the total population that can reach the green marker within the time threshold using the transport network. The dark blue region in @fig-proxi represents the **proximity population**. This is the total nearby population within the distance limit. Then, to calculate the total accessible and proximity populations, we count the population across all highlighted cells respectively. The **transport performance** of the network when travelling to the destination is then the ratio of the accessible and proximity populations (multiplied by 100 to convert to a percentage), as shown in @eq-tp:
@@ -44,7 +44,7 @@ Where:
 - $P_{access, i}$ is the total population that can travel to destination cell, $i$, within $t_{max}$ and $d_{max}$.
 - $P_{proxi, i}$ is the total population within $d_{max}$ of destination cell, $i$.
 
-This calculation is repeated, in this case across every destination cell, to construct the transport performance throughout an entire area of interest. An example of this for the Newport, Wales [urban centre] is shown in @fig-tp-newport.
+This calculation is repeated across every destination cell within an urban centre, to produce transport performance heatmaps for an entire area of interest. An example of this for the [Newport, Wales urban centre] is shown in @fig-tp-newport.
 
 ::: {#fig-tp-newport layout-ncol="1"}
 
@@ -54,9 +54,9 @@ Transport performance across Newport, Wales. Public transit within 45 minutes. T
 
 :::
 
-@fig-tp-newport shows how transport performance can vary across an area on a granular scale. The yellow/light green region indicates that ~50-60% of the surrounding population can reach the main city centre of Newport, Wales using public transit within 45 minutes. It also suggests that, in this case, the transport performance generally decreases closer to the outskirts of the urban centre. This means a smaller proportion of the surrounding population can reach the dark blue/purple areas using public transit within 45 minutes. Overall, it provides detailed, hyperlocal, insights into how the performance of the transport networks varies throughout an area.
+@fig-tp-newport shows how transport performance can vary across an area on a granular scale. The yellow/light green region indicates that ~50-60% of the surrounding population can reach the main city centre of Newport, Wales using public transit within 45 minutes. The transport performance generally decreases closer to the outskirts of the urban centre. This means a smaller proportion of the surrounding population can reach the dark blue/purple areas using public transit within 45 minutes. Overall, it provides detailed, hyperlocal, insights into how the performance of the transport networks varies throughout an area.
 
-Calculating transport performance requires several stages of input data processing and transport network travel time estimation. The methods and tools used by this Python package are discussed in more detail on the [Transport Performance: An Overview](../calculate_tp/index.qmd) page. For more insights on how to use `transport_performance` itself, check out the [tutorial](../../tutorials/index.qmd) and [API reference](../../reference/index.qmd) pages.
+Calculating transport performance requires several stages of input data processing and transport network travel time estimation. The methods and tools used by this Python package are discussed in more detail on the [Transport Performance: An Overview](../calculate_tp/index.qmd) page. For more insights on how to use `transport_performance` itself, check out the [tutorials](../../tutorials/index.qmd) and [API reference](../../reference/index.qmd).
 
 [euro-commission-paper]: https://ec.europa.eu/regional_policy/en/information/publications/working-papers/2022/low-carbon-urban-accessibility
 [urban centre]: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Urban_centre

index.qmd

@@ -26,9 +26,8 @@ which can make it difficult to understand these differences. This is typically
 because of computational complexity, transparency (closed-source and paid
 services), and data consistency (format and availability).
 
-To start resolving these issues, the `transport_performance` Python package
-bringing togethers a range of open source tools and research to allow everyone
-to:
+The `transport_performance` Python package helps to reduce barriers to
+transport analysis. It allows developers to:
 
 - Define an [urban centre] boundary based on population density;
 - Inspect, clean, and process [public transit timetable data][gtfs] and
@@ -37,11 +36,9 @@ to:
 [transport metrics][eurostat-paper].
 
 ::: {.callout-tip}
-
-Check out the corresponding [Docker image][tp-docker] 🐳!
-This aims to simplify both the dependency installation and end-to-end use of
+Check out the [transport performance Docker image][tp-docker] 🐳!
+This aims to simplify the dependency installation and end-to-end use of
 `transport_performance`.
-
 :::
 
 ## Where do I go now?
@@ -85,11 +82,11 @@ These docs are structured in accordance with the [Diátaxis][diataxis] framework
 
 ## Notes on contributing...
 
-We hope that the public, other public sector organisations, and other National
-Statistical Institutes can collaborate and build on this package. This will
-help improve both the national and international comparability of transport
-statistics whilst enabling higher frequency and more timely comparisons. You can
-find the `transport_performance` source code on [GitHub](https://github.com/datasciencecampus/transport-network-performance).
+We hope that the open source and public sector communities will collaborate and
+build on this package. This will help improve both the national and
+international comparability of transport statistics while enabling higher
+frequency and more timely comparisons. You can find the `transport_performance`
+source code on [GitHub](https://github.com/datasciencecampus/transport-network-performance).
 
 :::