Apartment price estimate function for Haaga

We introduced our Hedonic Regression modelling assignment in the final critique and for those who are interested about the result of our simplier model, the constructed function of price estimate (P) for the apartments in Haaga can be seen below.

𝑃 = 56416.52 + 2906.53 × 𝑠𝑞𝑚 + 36806.40 × 𝑑south + 15751.56 × 𝑑cood condition + 37438.10 × 𝑑more than three rooms + 𝜀

UGIS Practical 7: Nimbyism and wind turbines

In this practical we were analyzing wind turbine placement opinions of residents in Vuosaari (and Töölö, which is excluded from this post) and trying to find out if NIMBY-attitudes would be visible within the results.

Indeed, the strongest message from the respondents seemed to be that don’t assemble wind  turbines where ever we do spend our time (map 1). In other words, they don’t want to see wind  turbines. NIMBY -attitude alarm is going wild. On the other hand, who wants to see  them? They look awful, they have a terrible effect on the landscape and basically ruin  the feeling of being in nature. Thus, it is understandable from the respondents to think  that if these are needed, put them as far as possible from us. NIMBY -attitude might be  thought more like an unintentionally steered result by and from the questionnaire than a  phenomenon in this case.

Map 1.  Results of wind turbine placement survey for the residents of Vuosaari

The standard deviational ellipses, which were used as a visualization method, are supporting what is said above and make the  interpretation clearer even though missing to fulfill some key areas, for example,  Vanhankaupunginselkä as unpreferred or eastern Vuosaari as the preferred place. It is  heavily noted as an important and unpreferred place but not fit under the ellipse.

UGIS Practical 6: Accessibility

Jumbo shopping mall stands in very central location from metropolitan point of view and it is the best-selling mall in the whole Finland. Accessibility of this kind of place is an important factor when talking about sustainability of the metropolitan area.

In our analysis of rush hour accessibility, we can notice that Jumbo is very well accessible by car but with public transportation problems occur. Walking paths seems to be a bit problematic too as the map does not provide circle but radius changes a lot (map 1).

Map 1: Accessibility by walking

With car (map 2) you can reach the mall relatively easy from every direction, with 40-minute travel time is enough to cover almost the whole metropolitan area but with public transportation (map 3) even few kilometers raise the travel time over 40 minutes. Not even been harsh by any means, it seems unbelievable bad result for this kind of location and successful service cluster. Only areas near the southern parts of road 45 seems to offer a successful result as the travel time is under 30 or even 20 minutes with distance of six to ten kilometers. Also, areas near close to ring road three seems to have a decent connection to the mall. In the case of Jumbo cars are evidently dominating the way of transportation.

Map 2: Accessibility by car

Map 3: Accessibility by public transportation

UGIS Practical 5: Socio-spatial differentiation

1. Description of QGIS operations in the practical

In this practical we produced map on socio-spatial differentiation. In more detail, we tried to find out where are the people with both low educational level and low income living in the capital area.

First, we opened vector data about the socio-economic features in the capital area. The data was in form of 500-meter grids. In the data there were lots of non-valuable grids which were valued as “-1”. In order to create sensible analysis we converted those “-1”-values to zero with Select features by expression -tool. In the tool we made expressions to select the attributes containing relevant info (other than -1) about over 18-year olds, low educational levels and low incomes. After cleaning up the data we calculated the percentage shares of low income and low educational level in the grids with field calculator -tool. Then we used conditional statements -tool to add the calculated quartiles of percentages shares value of quartile, for example the lowest got value 1 and the highest got 4. Next, we selected the attributes containing value 4 in both of the examined category and saved the layer.

After having our new layer ready for map creation, we proceeded to make heat maps with default Heat map -tool in QGIS. At first, we did a heat map about all the jobs in Capital area. Settings in the tool were following:

  • Output raster: we made a reasonable name for the file
  • Radius: 1000 meters
  • Cell size: 20 x 20 meters
  • Kernel shape: quadric (biweight)
  • Use weight from field selected: we selected the sum of jobs
  • Output values: raw values

Finally, we finalized our maps with Map composer adding all the necessities to the maps.

In this exercise the phases with QGIS were relatively easy and straightforward, especially because we were already familiar with most of the tools used in this one.

2. Analysis of the maps & diagram

Firstly, according to constructed result (figure 1), people with low income and educational level (later LIE) seems to be located basically everywhere in the capital area. One might argue, from made point of view, that segregation cannot be found in a level to be mentioned. Still, when taking a look to higher concentration existence, the places where there is multiple LIE -grids near to each other, we might tell another story. It seems that the biggest probability for segregation related problems is at Eastern-Helsinki as the phenomena is clearly clustering most evidently there. The neighborhoods like Vuosaari, Mellunkylä, Kontula, Vesala, Itäkeskus and Puotila next to each other compose the biggest cluster.

In addition to the biggest cluster, we can see that other high concentrations are in the areas nearby Malmi, Kannelmäki, Hakunila, Espookeskus and Matinkylä. It should be noted again that the phenomena can be found in every city in the capital area with some clusters in each. In addition, it seems that every one of these areas are found to be just next to the main public transportation rail routes.

Figure 1: Map of the lowest quartiles of income and educational level in the capital area

The result is expected. Eastern-Helsinki and the areas near the railways in other parts of the capital area have had the reputation of social problems and uncomfortable feelings have been attached to their identity. Observations “on site” are clarifying the interpretation, train and metro station surroundings are usually the places where people with drug and alcohol related problems could be seen more often that anywhere else. It is still surprising how high the concentration seems to be in the Eastern-Helsinki. It raises also a critical question about the visualization, is it even too powerful and telling a bit more than it should. At least it gives a signal that the phenomena should be examined more carefully.

The fact that we can find grids with low income and educational level (over 30% of people have both low income and educational level) also in the areas like Westend, Kaivopuisto and Kuusisaari is also somewhat interesting. Who are these people living in the most expensive areas with that kind of statistics? I cannot come to any sensible explanation with my first or second thought when thinking about both variables at the same time. One would be a lot easier to explain.

Social segregation is one of the biggest issues in urban studies and phenomena that is seen very negative from many points of views. Segregation is claimed to be in relation with higher risk of violence, health problems and person’s risk of dropping out of education and work life. When analyzing segregation, there are lots of variables to take into consideration. Income and educational level are, in my opinion, definitely variables to take a close look. Also prior language, cultural background, health service use and household size might be important factors as well.

Mid-term critique – do we have a case?

What kind of case we are talking about?

During the first quarter our USP -group have been observing and analyzing Haaga from numerous different perspectives and it is time to take a deep breath and look back to see what is been noted. History is one kind of foundation for the planning and it has been discussed in many occasions. We have learned how Southern Haaga, as it nowadays stands, was constructed on the area of Villas of which most were demolished. This old nostalgic architecture does not exist in great power anymore even if something of that is still visible (if really closely looked). It seems that history is not going to be playing in key role in future planning of Haaga.

Going into technical analysis, we have noticed that building density, the efficiency ratio, is relatively low in Haaga giving us a weak signal about infill potential. There has been some outside-the-group evidence also that there really might be possibilities. Still, local and on-site made perception about this potential seems to be lacking.

Low density is often connected with poor service offering and not surprisingly, urban lifestyle is seen more or less missing from Haaga. The few street level shops, cafes and other services are struggling, but even more, they are almost non-existent. At least these services are spread in a way that does not construct a lively cluster of services.

Yet we don’t have statistics where people are commuting from Haaga, but we sure know, that they have good public transportation connections with two train stations and highway bus connections as the main hubs. When combining public transportation connections with what is said about density, again it drives me to think about infill potential.

We have also gathered a data about the people in Haaga and the neighborhood seems to be some what balanced with all the age groups even though elderly group is weighed a bit. Moreover, income or educational level are not showing any strong signals to any direction, neither is multicultural backgrounds.

Finally, it is needed to say that Haaga is perceived to be a nice, green, comfortable and wanted area to live by locals. That is the most important thing to keep in mind when thinking about the future of the neighborhood.

How to determine the direction of future planning of the area?

Firstly, it would be interesting to try to observe the real infill potential on-site. This far I have been limited to bird view observations and I noticed that the area is challenging considering infill potential with enough big volume to make any difference. Northern railways station is creating a service centralization but toward inner Haaga it is blocked by multiple service housing companies for elderly. Near the old shopping mall there might be possibilities to create service-filled development but at the same time it is important to notice that shopping mall Kaari is located just north to Haaga touching its borders. The mall is a challenge for northern part to create service-active environment but still, that area along Näyttelijänkatu is somehow fascinating.

On the other and, during the observations one might also narrow the question just to renovation needs of current buildings, because as we know, housing companies are sometimes able to gain economic value by infill construction during the renovations with the help of the city (to learn more, check link 1). The information about renovation plans and their locations in the area might help us to understand the potential more deeply.

Before the on-site observations it could be a good idea to find out what are the current service locations and examine these popped-up places even more carefully (example below). As we know, Haaga is a good place and it surely has its best places to spend time, meet people and enjoy services. That kind of culture is something that should be supported instead of creating something totally new.

To continue my preferred way of supporting and enriching the local culture inside the area, it would be interesting to make soft-GIS questionnaire about the best and the worst places and things of Haaga for locals (to learn more about soft-GIS check link 2 below). This might be supported by some kind of train station events and face to face interviews especially with elderly and other groups not that easy to catch via internet. As we would be learning about preferred places in the area we could also start built justification for new development construction in the places perceived as the worst. This might lead to better mutual consensus between planners and locals ending up with less drama.

In the introduction part I mentioned a problem with economic incentives for the common good. As it is clear, that purely from economic point of view it is suggested to supply more housing in this kind of well-located and wanted area, the question remains: what amount of additional supply is meaningful? There is a risk that we force additional construction in the area with difficult urban structure (in this sense) without getting really a meaningful positive economic effect especially when comparing to negative effects to surroundings.

To sup up, there is many interesting things noticed about Haaga and many of them should be examined more carefully. The formation process of urbanity is examined all over the world currently, especially in Europe, as urban sprawl is seen one of the main challenges in our growing cities. It is visible that we are on the track of European vision of future cities as well as the objective of the metropolitan area. When coming to data collecting methods, soft-GIS have been maybe more Finnish way of learning the urbanity from the people in their living environment, as in comparison, observations being the European way to do it. In my idea we would combine them.

I must admit that I am feeling a bit of dizziness as this mid-term came so fast. That is why the research question is not formed as maybe expected and why I end my inside-the-head wondering to this and leave additional discussions to our next meet-up which I am really looking forward to.

Links:

  1. https://www.vtt.fi/inf/pdf/technology/2013/T97.pdf
  2. https://www.gim-international.com/content/article/softgis-methodology

UGIS Practical 4: Heatmaps

What’s up this time with QGIS?

In this practical we produced maps on spatial distribution of jobs, one through all industries and four within different selected industries in the capital area. The selected industries were commercial, industrial, scientific and real estate. First, we opened vector data about the job locations in the capital area. The data was in form of 500 x 500 meter grids. In the data there was lots of non-valuable grids which were valued as “-1”. In order to create sensible analysis we converted those “-1”-values to zero with Field calculator and its tool called Conditional statements. Conditional statements -tool is automated find and replace -tool which finds every unwanted value and replace them with preferred one. To be precise, we made find- and replace actions only for columns (industries) we were analysing and created new attribute column for corrected values.

After having our attribute tables ready for map creation we proceeded to make heat maps with default Heat map -tool in QGIS. At first we did a heat map about all of the jobs in Capital area (map below). Settings in the tool were following:

  • Output raster: we made a reasonable name for the file
  • Radius: 1000 meters
  • Cell size: 20 x 20 meters
  • Kernel shape: quadric (biweight)
  • Use weight from field selected: we selected the sum of jobs
  • Output values: raw values

Secondly, we did rest of the maps within the selected industries (commercial as an example below). Settings in the tool were mostly same, only the weighted field varied depending on what industry we were working on.

Finally, we finalized our maps with Map composer adding all the necessities to the maps.

In addition, we did a diagram with MS excel. To do that we needed sufficient data from our vector layer. In order to get what we wanted, which was spatial distribution of jobs in Helsinki, we selected all the the attributes within Helsinki and saved them as own layer using Save selected features -tool. Then we opened our saved file in dbf -format with excel and created a wanted diagram.

In this exercise the phases with QGIS were relatively easy and straightforward, the biggest questions raised again about the things behind operations. What is the Heat map -tool really doing, what all the numbers it produces mean and how to visualize them? It was interesting to notice the uncomfortable feeling when trying to select what to tell in legend.

Analysis of the maps & diagram

The five finished heatmaps provide fantastic visualization of workplace distribution in Helsinki capital region. Each heatmap tells its own story, but they can be roughly separated into two categories: a) both highly local and spread and b) heavily clustered and centered in few locations.

Both industrial and commercial job distribution maps show plenty of activity around Helsinki capital region. With industrial jobs, this probably means factories, logistics centres and office complexes, located in established industrial zones and in proximity of motorways. Industry tends to avoid the pricey land of the city centre (with the exception of Hietalahti dockyard, which I will further examine later).

Commercial jobs are centered in large shopping malls and they too have good connections to motorways. Helsinki city center (which has its own share of shopping centers, too!) is the largest single cluster of commercial activity, but the shopping center of Jumbo in Vantaa doesn’t seem to be far behind. Of the five sectors we compared in this exercise, commercial jobs are most evenly spread. This likely is the result of people’s desire to do their daily shopping close to where they live.

An interesting possibility to continue from this study would be to compare the spatial distribution of commercial and industrial jobs historically, say between 1980 and 2010. In recent decades, many industrial workplaces have disappeared from the Helsinki city centre and relocated to the outskirts of the city (especially by the outer ringroad), that provide good connections and cheaper land. The major exception to me is the Hietalahti dockyard, where they continue to build ice-breakers to this day. Ship-building is one of the few labor-intensive industries remaining within Helsinki borders. Helsinki, hoping to preserve part of its industrial past, supports the dockyard’s operation in its master plan for decades to come.

A different picture emerges from looking at the scientific and real-estate jobs distribution maps (the real-estate industry was our optional choice). These two industries are heavily centralised in the centre of Helsinki and other, very limited areas. The scientific industry hotspots are centered at the university campuses in city centre, Meilahti, Viikki and Otaniemi. Minor activity can be found throughout capital region, probably supported by private sector employers. Many of those companies would probably be attracted to the clusters created by the universities, which makes universities the driving force of the scientific sector in Helsinki region.

Real estate job distribution is also the most active in the city centre, where the land is, of course, dearest. There are many smaller hotspots in local centres such as Herttoniemi, Malmi, Tikkurila and Leppävaara. In that regard, real estate job distribution map is similar to the commercial jobs map. Unlike in that map,one of those minor real-estate hotspots can match the colored carpet of the city centre.

The total job distribution map falls somewhere in between the two categories. It reveals that workplaces exist throughout the capital region. On the other hand, the deep red carpet covering Helsinki city centre show that the activity is heavily centralized. A few otherhotspots in Pitäjänmäki, Keilaniemi, Tikkurila and Helsinki-Vantaa airport complete the picture.

The diagram depicting industry structure by workplaces  reveals that Helsinki is a true multi-industry city with no dominant sector of industry. Governance services and hotels and restaurants seem to be the largest sectors, but neither exceeds 1/6 of the total share. Historically, administrative sector has been important for Helsinki as it has never been known as an industrial town. The industry that it had has since relocated to surrounding cities, confirmed by the low share of the sector in the diagram. Those workplaces have been replaced by large scientific and information sectors, for which the university educates a skilled workforce. The relatively large share of international organizations is also worth noting, while it supports Helsinki’s claims of being a multicultural city.

The heatmaps reveal that most industries have clustered or at least have some activity in city centre. According to the maps,Helsinki city centre embodies that multi-industry city depicted in the diagram. The other clusters in the area can be very strong in one industry (such as scientific sector of Otaniemi) but severely lacking in another (for example realestate sector in Otaniemi). The challenge for the future is to duplicate the diverse job structure of the city centre to other districts in the region.

Haaga – A nightmare for Economists?

Haaga is known for its calm atmosphere and vast green areas. Historically it has it’s background in Villas from which it developed to an area with lots of small multi-storey buildings. Fast urbanization during 50s and 60s built northern part with a bit bigger scale but still mostly maintaining same kind of characteristic as older parts. When analyzing the housing density in Haaga and surroundings, we can find out that density is relatively low compared to its neighbors (figure 1). It could be highlighted even more because of closeness of railway connection toward center.

Figure 1: Density in Haaga (kem2/m2 in 250m grids)

From economic point of view the housing density should be higher when housing demand rises or business activity level rises. Firstly, if density is relatively low when demand is high, the prices will rise leading to unfair and economically unbalanced situation from which almost everyone, but the current owners of housing suffers. Secondly, from the same reason, the economic activities will not be able to gain all the positive effects of the cluster effect that usually occurs when density rises.

After all the above mentioned, it seems that Haaga really is more like nightmare than happy dreams for economists. Area has great demand nearby center business district and good public transportation connections and nevertheless, density of low efficient suburban area. The prices must be sky rocketing.

Helsinki region transport examined interconnections with land use and transportation planning in 2015 (MALPAKKA -report, link below the post). They came to conclusion that there is huge need of infilling in Helsinki when reflecting to regional level land use objectives and not surprisingly, showed that Haaga is one area with huge potential of infill construction (figure 2).

Figure 2: Infill potential according to Malpakka -report

Later on during the on going autumn it will be more than fascinating to explore more this wicked confrontation with current strong identity of the area and economic suggestions related to it.

Links:

https://www.hsl.fi/sites/default/files/uploads/malpakka-raportti.pdf

UGIS Practical 3: the building efficiency ratio and urbanity

What we did?

Firstly, we constructed a map of the building efficiency ratios in neighborhoods of Helsinki (below).

Secondly, we constructed a map of the building efficiency ratios in 250*250 meters within Helsinki (below).

When talking about the easiest and most difficult parts of this practical, the conversation steers to visualization selections and their strong impact to the validity of the information. The most difficult part was indeed the selection of proper graduating intervals so that it shows us sensible information. The easiest part was combining the attributes of used layers and doing the field calculations to which had gathered, to our surprise, even a hint of touch.

What the maps were telling to us?

The map comparing regional efficiency ratios in Helsinki areas is a nice explanatory tool. Comparing regional differences gives one a good overview in general but lacks more detailed information. The obvious weakness of the map is that the ratio tells nothing about the building types (aside from a short description in the legend). In case of Helsinki this is problematic, since the map shows semi-detached houses and rowhouses being the most common type of housing in Helsinki while in reality that is not the case. A neighborhood consisting mainly of apartment buildings among large parks might have the average ratio of a “semi-detached house neighborhood”.

250 x 250 m grids or finer area units than districts give more detailed and comparable information of the efficiency because grids are equal sized and built environment that is not buildings, e.g. parks and football fields, are affecting less on the calculated values. The greater area unit we are focusing, the flatter values we get since it is always an average of several features within the area unit. Despite this, both maps tell the same story, highest building efficiencies are found in the downtown, but using the grid level it is possible to examine the variation also within the districts.

When the efficiencies were calculated with the districts, the highest ratio value any district received was 2.10 in Kamppi and the second highest was clearly lower in Punavuori 1.68. When calculating the same ratios with the 250 x 250 meter grids, variation is notably wider and because of that we created an extra class (2.10-3.64 Most dense apartment building blocks): highest grid value 3.64 was found in the Kluuvi district and totally 10 districts included one or more cells where the ratio is above 2.10. Therefore, many spatial phenomena should be examined in different area units. One example of the importance of using suitable area units can be raised from Vaattovaara & Kortteinen’s segregation studies: the concentrations of underprivileged people in Helsinki region are found in single housing companies or even in stairways, which couldn’t be observed if the area unit used is too large (e.g. blocks or districts).

Measuring urbanity with the building efficiency ratio

Building efficiency ratios are very useful in comparing different districts densities. The ratio gives an idea how densely built area is. Maps showing more detailed efficiency ratios can point where the most urbanized areas of the city are.

However, urban is not all about density and a high building efficiency ratio tells little about the neighborhood’s character. Sure, a dense neighborhood offers more encounters between residents, which is essential for an urban environment. But an urban area should offer a wide variety of services. A business district that is dead silent after 5 pm in a Friday or a residential area with only high-rise apartment buildings and green areas between them are hardly urban.

Transition of Haaga during the past century

When analyzing neighborhoods, it is always fascinating to hear what the locals think about their home environment. I found an interesting newspaper article (link can be found below the post) about the differences between South- and North-Haaga. It included interviews of residents from both side of the Haaga and todays statistics of the area.

How do this article and the historical growth of Haaga get together? In many ways. Firstly, the locals had pretty good knowledge about the history of their neighborhood. They said that South-Haaga has “the city of villas” structure from beginning of 20th Century as a bottom layer of build environment while North has its structure from fast built phase after 40s. Indeed, when checking pictures below taken from the air (https://dev.hel.fi/ilmakuvat) in 50s and 60s, we can notice, that North-Haaga was mostly build during those two decades. This difference is found to be most visible in more varying street structure in South and in bigger buildings in North.

 

Air photos of Haaga: 50s on left and 60s on right

Secondly, when analyzing the growth, its interesting to get into the similarities of both sides of Haaga. The locals interviewed seemed to be happy that these areas really form a unite neighborhood. The share of green areas, calm atmosphere and age distribution were founded as the main similarities. To similarities I must add right away that the railway is just centered between these two parts of Haaga balancing the accessibility to and from the city center.

Thirdly, when thinking about the word of the week, transition, I would like to look a bit more closely to those mentioned villas of Southern part. The local tells that the area still have some marks of villa period. During our semester starting bike excursion we were able to see that there were few of the old villas still left in the area but with some struggles to form a coherent urban structure as some of the newer and bigger buildings were build somewhat without an eye for a landscape. Still, it is great to notice that few of these villas have been able to survive the transition and remain as a tight link to the history of the area. It seems that cultural sustainability had its place in part of the past century transition of Haaga.

Links

”Mikä Haagoja yhdistää?”, Helsingin uutiset, 19.3.2015. Available at: https://www.helsinginuutiset.fi/artikkeli/273098-mika-haagoja-yhdistaa

UGIS Practical 2: biking tour for tourists

1. Description of QGIS -phases

The practical started with getting familiar with given vector data about historical points and areas of interest. I chose Cultural heritage areas to get information about historically interesting buildings within the area I was interested in.

When it comes to QGIS -operations in this practical, we used digitization of route, points of interest and areas with points, lines and polygons using OpenStreetMap as a reference map. The practical continued with few calculations with Field calculator -tool as the length of the route and the area of free exploration area was calculated. Finally, we used different visualization methods like different symbols, fill types and transparency levels to get the map look polished.

Most difficult part of the task was obviously planning the route, but luckily, I got a clear idea immediately after starting the practical. I would have liked to add a bit more beautiful version of the background map but as always, time was limiting the possibilities. While executing the practical I got stuck only once when trying to find the right place to change the symbols of each point of interest one by one but got through it with help of a friend.

My final map of the planned biking tour is seen above (figure 1).

Figure 1: The final map of the tour

2. The backgrounds of route choice

The chosen route was designed due to my own interest toward the area. We went through the area with our USP student group during our first week’s bike excursion and I got especially fascinated about the Kiosk of Käpylä, its long history and civil activism that saved it from demolishing. It is such a great example how meaning of a place attaches the people to the place giving the sense of community and home.

The first point of interest, before the Kiosk of Käpylä, an old anti-aircraft gun gives a good concrete and interesting start, especially if there are children joining the tour. The third point of interest is a beautiful wooden Käpylä which has also interesting history with garden city thinking behind the planning and demolishing plans during the 1960s which were luckily abandoned by angry feedback of citizens.

The Olympic Village which was never used as an Olympic Village is just wicked part of our building history. It is not maybe the most fascinating by its architecture, but the history side is truly interesting and at the end, it was just perfectly located along the trip. Finally, the Old town was a self-justified selection as our context was a tourist trip. This also gives an opportunity to use an interesting marketing slogan of “the most unvisited old town in capital cities” or something similar.3.

3. Commercial description of the tour

Welcome to our Historical biking tour of Käpylä, the Olympic village and the Old town. In this trip you will get familiarized with Finnish garden city thinking of 1920, Olympic and war history of Finland and with one of the most unvisited old towns in capital cities.

Our main attractions are an old anti-airgraft gun at Taivaskallio, the Käpylä garden city, the Olympic village for the never-held summer Olympics 1938 and the old town of Helsinki.

This trip is suitable for everyone able to bike and the speed is going to be really relaxed, so you don’t need to be in shape to enjoy this tour. The total length of the tour is a bit below 8 kilometers and it is scheduled to take 4,5 hours with spoken introductions to the points of interest.

The tour is arranged every Saturday starting at 10 am if enough participants enroll in. Pre-enrollment is needed, and it should be done latest at Thursday evening 12 pm via email. We will send confirmation email about the trip on Friday morning until 10 am.