Cécile Saint-Marc
email: cecile.saint-marc@imag.fr

Paule-Annick Davoine
email: paule-annick.davoine@imag.fr

Marlène Villanova-Oliver

Dealing with the Imperfection of Historical Hazards Data in GIS Limits and Solutions to Make Chronological Maps for Decision Makers

Keywords: historical data, natural hazards, imperfection, uncertainty, chronological map

Natural hazard studies are often based on the spatial analysis of remarkable past events. However, historical information contains imperfections in its spatial and temporal locations and in the description of events. In this paper, we want to show the benefits of a system approach, that take the context of use of the visualization into account during the early phase of storing up data in the GIS, in order to make maps with imperfect data. We base on a concrete example: making a map of the chronology of phenomena during a past flood event, for decision makers who are lay users in cartography.

Introduction

The study of natural risks is often based on the understanding of remarkable past events, with the aim to prevent upcoming hazards. In that context, geo-historical data is used. But this kind of data has many imperfections: it comes from testimonies or archives and so it is heterogeneous, often poorly structured and imperfect in the description, location and dating of events [START_REF] Moisuc | Designing Adaptive Spatio-temporal Information Systems for Natural Hazard Risks with ASTIS[END_REF]. The interest of Geographic Information Systems (GIS) has been demonstrated in the field of risk study [START_REF] Konecny | Geographic Information and Cartography for Risk and Crisis Management[END_REF]. However, this tool has two main limits in the context of historical data. Firstly, the temporal dimension was only recently implemented in GIS tools and is still poorly incorporated. Secondly, even if rules to visualize uncertainty exist ( [START_REF] Dragicevic | A Fuzzy Set Approach for Modelling Time in GIS[END_REF], [START_REF] Thomson | A typology for visualizing uncertainty[END_REF], [START_REF] Maceachren | Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know[END_REF], [START_REF] Arnaud | Temporal geovisualization in risk area[END_REF], [START_REF] Senaratne | Usability of Spatio-Temporal Uncertainty Visualization Methods[END_REF]) and may be applied in classical GIS, imperfection is not treated differently than other attributes of objects. The use of imperfect data in a GIS is a twofold issue: 1) during the modelling and the storage of the data, 2) to include imperfection in visualization. Our work is based on a case study: the chronology of the flood of October 1940 on the river Le Tech, in the South-West of France. We endeavour to visualize the impacts of major historical floods on the railway system. It requires storing up in the GIS data about the impacts of floods, their causes and responses to the events (decisions that were taken, reconstruction work). The information is intended to be visualized by decision makers who are not used to use complex maps. This perspective of use had an important impact in our approach. Indeed, [START_REF] Scaife | External cognition: How do graphical representations work?[END_REF] proposed to adapt the visualization to the users' level of experience with graphic displays and to the type of tasks the displays would have to support.

In this paper, we present the dimension of imperfection in geo-historical data, and propose some solutions to deal with the limits of GIS. Then we explore a chronological visualization of events and some ideas to show imperfection in the map.

2

The Dimensions of Imperfection in Geo-Historical Data

The information concerning risks is multidimensional. It incorporates spatial (location and spatial development), temporal (date, duration, return period), informational (description) and also contextual dimensions (the context of the event) [START_REF] Davoine | SIHREN Conception de Systèmes d'Information Spatio-Temporelle dédiés aux Risques Naturels[END_REF]. Imperfection may be found in each one. For example, in the historical data about the flood of 1940 on Le Tech river, we found: "At 4:30pm, le Tech reached almost the underside of the bridge" (vague description), "during the flood from October 17 th to 20 th of 1940, most of the railway have been destroyed" (imprecise dating and location of impacts). So it is difficult to reconstruct the phenomena and their chronology.

Besides, every phenomenon has a limited reliability because of the nature of sources of information (testimonies, sometimes collected a long time after the event, thus inducing subjectivity). Moreover, the information is incomplete because some clues of old events have disappeared.

While constructing our information system, we chose to store imperfection in natural language (qualitative format) in order to distinguish the different kinds of imperfection. Every field of the GIS layer relative to a dimension of the information was lined with a second field concerning its imperfection.

Several classifications exist to characterise the kind of imperfection affecting data ( [START_REF] Smets | Imperfect Information: Imprecision -Uncertainty[END_REF], [START_REF] Kraft | Vagueness and Uncertainty in Information Retrieval: How can Fuzzy Sets Help? IWRIDL[END_REF], [START_REF] Bouchon-Meunier | La logique floue[END_REF], [START_REF] Gschwandtner | A Taxonomy of Dirty Time-Oriented Data[END_REF], [START_REF] Maceachren | Visual Semiotic and Uncertainty Visualization: An Empirical Study[END_REF]). We chose to use the classification of [START_REF] Snoussi | Methodological proposals to handle imperfect spatial and temporal information in the context of natural hazard studies[END_REF] that concern a large set of imperfections and that was built specifically to characterise spatial and temporal imperfection of natural risks data. It is presented in Figure 1. In our dataset, we can see that 50% of the dates are imprecise or uncertain, that lead to difficulties when we want to study the temporality of phenomena (Table 1).

3

Limits of Classical GIS to Manage Historical Information about Natural Risks

Semantic versus Geometric Heterogeneity

To store our data, we used 'classical GIS': ArcGis©, QGIS©. In these GIS "one theme is represented by one layer of geographic information, which is a group of elementary objects having the same geometry (point, line or polygon)" [START_REF] Sanders | Système d'information géographique (S.I.G)[END_REF].

In our GIS, we had to deal with objects that were impacted by floods, with diverse geometries (e.g. station, rail track, city centre, mountainside). We had two options: either to create three layers for the three kinds of geometries or to create a single layer. We chose this second option and stored the impacts as points in the GIS, such as markers that indicate "that object was damaged". This solution had the advantage of keeping the thematic data-logic of the GIS. Second, we thought that punctual locations lightened the map load and so lead to a clearer map for users than polygonal geometries, even if it had not been tested at this time. To change precise linear or surface geometries into points introduce more imprecision in the data. However, we assume that storing punctual locations as polygons would have introduced the same level of imprecision.

For precise geometries, we placed the points at their barycentre or at the nearest point inside the surface or the line. For fuzzy locations, the point was located randomly in the possible area of location, or at the most probable location according to the topographic context when it was possible.

Date of phenomena and visualization

A set of tests with policy makers, led by [START_REF] Cliburn | Design and evaluation of a decision support system in a water balance application[END_REF], show that this audience finds it useful to visualize uncertainty of data, but wants to see information with no ambiguity ("is the future global water balance a problem or not?"). These two statements are paradoxical. We think that this need could be answered by suppressing as much as possible the imperfection in the appearance of 'raw' data, while it is notified in an other way (alternative map, pop-up window). We excluded the idea to assign a period of time (maximum and minimum possible dates) to damages on the rail infrastructure that were a priori one-time. As a consequence, we assign an only dating to each phenomenon of the flood, even if its real date is fuzzy. We also excluded the fuzzy set theory [START_REF] Zadeh | Fuzzy sets[END_REF] because it was shown that lay users tend to ignore statistical probabilities when making decisions [START_REF] Tversky | Judgement under uncertainty: Heuristics and biases[END_REF].

When the date of a phenomenon was missing, we excluded to assign no date in the GIS because, in an animated-map context, this phenomenon would never appear in the visualization or always stay on it. We used the hydroclimatic context of the flood to deduce probable dates for events. The imperfection of the date was recorded in the attribute table. For example, [START_REF] Soutadé | Quand la terre s'est ouverte en Roussillon, l'Aiguat-octobre 1940[END_REF] dealt with a "mudslide in Le Firal" during the flood, but did not write its date. The flood peak of Le Tech River was on October 17 th and 18 th of 1940, and the rain intensity was maximal on October 17 th . So we assigned the probable dating of October 17 th to this phenomenon.

Temporal precision is different between periods

Historical events are described hour by hour in monograph about the event, such as in [START_REF] Soutadé | Quand la terre s'est ouverte en Roussillon, l'Aiguat-octobre 1940[END_REF]. However, since the beginning of the decrease of the watercourse level, the temporal granularity decreases to a daily unit. And when the situation become normal again, the date is even rarely quoted. In fact, the temporal precision of data depends on the historical period described by the testimonies. Nevertheless, in our context, it is important to consider the phenomena that occurred after the flood, such as the approach used to restore the traffic. On the one hand, for an event occurred during the flood period, we considered that "in the afternoon of October 18 th " is an imprecise dating, because it is difficult to sort in a chronology with "on October 18 th at 4:30pm". On the other hand, we considered that a dating with a precision of the day, during the post-flood period, was precise because there was rarely more than one event per day in that period. Then, if two events happened the same day in that period, we considered that they happened at the same time.

First map of a Historical Chronology

Our data structuring work results in a first map of the chronology of the flood's impacts, which is presented on Figure 2. Several phenomena overlay spatially across time on the map. As a consequence, we chose to figure the dating with the variable 'size', in order to sort overlaid objects by size, as it was proposed by [START_REF] Bessadok | Automatic evaluation and improvement of map readability[END_REF] in the use case of surfaces. That permits to see together the older phenomena (the bigger points) behind the more recent ones. Even if the variable 'size' is useful to handle overlaying, it is not adapted to visualize qualitative data [START_REF] Bertin | La sémiologie graphique[END_REF]. Indeed, it seemed to us that it was difficult to link visually the absolute size of a point from the map to the legend. That is why we also added the variable 'colour': two different hues distinguish the rise and the decrease of the watercourse level, while the intensity of colour increases near the peak of the flood. However, this first map do not show the imperfection of the data. To represent together the value of data and its imperfection is still an open research issue [START_REF] Maceachren | Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know[END_REF]. Some authors proposed solutions to map several kinds of imperfections, together with a quantitative attribute of the data [START_REF] Arnaud | Temporal geovisualization in risk area[END_REF]. But most of the current studies consider only one type of imperfection on each map, or even different representations per subtype [START_REF] Maceachren | Visual Semiotic and Uncertainty Visualization: An Empirical Study[END_REF]. How could we visualize imperfection clearly for decision makers, if different kinds of imperfection exist together in the dataset? And what if several dimensions of the dataset are tainted jointly with imperfection? Some courses of actions exist to answer these questions, such as the use of interactivity to explore the pairs {type of imperfection, dimension of information} one after the other. We also think that graphic visualization next to the map could be of interest to show the level of imperfection for each point of the map. Using charts involve to transform qualitative imperfection into quantitative data. Figure 3 shows two examples of charts, that we draw by giving a mark out of ten to each subtype of imperfection, according to its supposed impact on decision making from the map. Thus we turn temporarily the qualitative data into an ordered one, that provides information on the seriousness of imperfection towards the analysis. Of course, the sequence of ranking of the subtypes of imperfection depends on the context of use. This order should be objectivized thanks to a survey of users.

Conclusion

Through several examples from a concrete use case, we have tried to show that the construction of a historical GIS raises issues, the answers of which are largely influenced by the context of use of data With the aim to present the impacts of a flood, we stored imperfections in a qualitative format. We assigned a single date and location to each phenomenon of the flood, even if these parameters were imprecise or uncertain, in order to simplify the spatio-temporal visualization. This simplification should not prevent from including the dimension of imperfection in the visualization.

Nevertheless, there are still open issues. Some of these are inherent to the historical aspect of data and cartographers have to adapt. Others are relative to the cartographic visualization. In front of the limitations of GIS to deal with the temporal and uncertainty dimensions, we use empirical approaches, which do not follow the rules of graphic semiology.

Fig. 1 .

 1 Fig. 1. Taxonomy of imperfect information ([19], reproduced by permission)

Fig. 2 .

 2 Fig. 2. Map of the chronology of impacts of the flood of October 1940 on the railway system in Le Tech watershed

Fig. 3 .

 3 Fig. 3. Visualization of the imperfection of data for the three dimensions of information (time, space, attribute) thanks to graphs

Table 1 .

 1 Typology of imperfections in our historical dataset


Adrien Barbaresi
email: adrien.barbaresi@oeaw.ac.at

Collection and Indexing of Tweets with a Geographical Focus

Keywords: Computer-Mediated Communication, Web Corpus Construction, Database Solutions, Visualization

This paper introduces a Twitter corpus currently focused geographically in order to (1) test selection and collection processes for a given region and (2) find a suitable database to query, filter, and visualize the tweets. Due to access restrictions, it is not possible to retrieve all available tweets, which is why corpus construction implies a series of decisions described below. The corpus focuses on Austrian users, as data collection grounds on a two-tier detection process addressing corpus construction and user location issues. The emphasis lies on short messages whose sender mentions a place in Austria as his/her hometown or tweets from places located in Austria. The resulting user base is then queried and enlarged using focused crawling and random sampling, so that the corpus is refined and completed in the way of a monitor corpus. Its current volume is 21.7 million tweets from approximately 125,000 users. The tweets are indexed using Elasticsearch and queried via the Kibana frontend, which allows for queries on metadata as well as for the visualization of geolocalized tweets (currently about 3.3% of the collection).

Introduction

The availability and ease of use has made the online social networking service Twitter one of the most popular data sources for studying social communication [START_REF] Leetaru | Mapping the global Twitter heartbeat: The geography of Twitter[END_REF]. Generally, the interest in Twitter is considered to reside in the immediacy of the information presented, the volume and variability of the data contained, and the presence of geolocated messages [START_REF] Krishnamurthy | A Few Chirps about Twitter[END_REF]. Other social networks do not deliver the same amount of text, especially for German [START_REF] Barbaresi | Collection, Description, and Visualization of the German Reddit Corpus. In 2nd Workshop on Natural Language Processing for Computer-Mediated Communication[END_REF], and more importantly, cannot be deemed as stable in time in terms of popularity and API access [START_REF] Barbaresi | Crawling microblogging services to gather language-classified URLs. Workflow and case study[END_REF]. Short messages published on social networks constitute a "frontier" area due to their dissimilarity with existing corpora [START_REF] Lui | Accurate Language Identification of Twitter Messages[END_REF], most notably with reference corpora. Since August 2009, Twitter has allowed tweets to include geographic metadata [START_REF] Stone | Twitter Blog: Location, location, location[END_REF], which are considered to be a valuable source for performing linguistic studies with a high level of granularity, e.g. on language variation [START_REF] Ruiz Tinoco | Twitter como Corpus para Estudios de Geolingüística del Español[END_REF]. Thus, from the point of view of corpus and computational linguistics, Twitter data are both highly relevant and difficult to process. Due to access restrictions, mostly mechanical constraints on the API, it is not possible to retrieve all tweets one would need. For example, when using the so-called "gardenhose" streaming API, it is necessary to enter search terms or a geographic window, and a fraction of corresponding data is returned, which may greatly affect results [START_REF] Morstatter | Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose[END_REF], especially for highly frequent keywords as used by the TweetCat approach [START_REF] Ljubešic | Tweet-CaT: a Tool for Building Twitter Corpora of Smaller Languages[END_REF] or for the German Twitter Snapshot [START_REF] Scheffler | A German Twitter Snapshot[END_REF]. In that sense, focusing on a given geographical region can be a way to provide enough relevant linguistic evidence. However, there are structural characteristics which complicate the collection of tweets from German-speaking countries, and especially Austria, which makes it a interesting test case. First, even without considering the market penetration of Twitter, the population of the country is comparatively small, so that Austrian users cannot be expected to be easily found at random, all the more since users preferentially connect to other users from their own country [START_REF] Kulshrestha | Geographic Dissection of the Twitter Network[END_REF]. Second, geolocated tweets are a small minority, with estimates as low as 2% of all tweets [START_REF] Leetaru | Mapping the global Twitter heartbeat: The geography of Twitter[END_REF]. Third, because of privacy concerns Austrian users can be expected to be very cautious about geolocation services: German twitterers for example are very reluctant to include geographic coordinates in their tweets [START_REF] Scheffler | A German Twitter Snapshot[END_REF]. Finally, the success at being able to place users within a geographic region varies with the peculiarities of the region [START_REF] Graham | Where in the world are you? Geolocation and language identification in Twitter[END_REF].

Design decisions

Following the characteristics stated above, and because corpus construction in the linguistic tradition implies a number of decisions which have to be made explicit (Barbaresi, 2015a), salient methodological issues will be dealt with in detail in this section. First, while most studies ground on a collection process which is limited in time, the corpus described in this article is a monitor corpus in the sense that it grows constantly with time. Since metadata include the time of posting, it is possible to split the corpus in units of time. More generally, the purpose is to be opportunistic enough during corpus creation in order to enable researchers to tailor subcorpora which match particular interests. Second, geolocated tweets (place element in the JSON response) may be casually sent from Austria, but not really by Austrian users: they can merely be an indication that the user has spent some time in Austria. Furthermore, it is technically possible to spoof one's location either by editing by hand the location field of a given tweet, or by tampering with the GPS device used for geolocation. On the other hand, the field which is sent with each tweet along with the user profile (user/location field), if given, refers to the subjective point of view of the users as regards their lo-cation. It may not seem as objective as mere coordinates, and even when both the profile and the device location are valid, they do not always correspond [START_REF] Graham | Where in the world are you? Geolocation and language identification in Twitter[END_REF] but it is a strong assertion regarding the place users feel at home or related to at least. Here lies the difference between a mere "posted from Austria" predicate and the corpus construction process which leads to tweets hopefully "made in Austria". Third, since language cannot reliably be used as a proxy for location [START_REF] Graham | Where in the world are you? Geolocation and language identification in Twitter[END_REF], no language selection is undertaken. For the same reason, retweets are included, even if the original messages may have been posted from other locations and in another context, because they are still considered to be meaningful. They can be removed for further studies by using the metadata as well as the "RT" mentions in the messages [START_REF] Ruiz Tinoco | Twitter como Corpus para Estudios de Geolingüística del Español[END_REF]. Furthermore, the use of typical Austrian-German words do not seem to lead to a substantial amount of users, due to the mobility of users and due to the difficulty to define a "national variety" [START_REF] Ebner | Duden: Österreichisches Deutsch[END_REF], which separates this case from languages like Croatian or Slovene [START_REF] Ljubešic | Tweet-CaT: a Tool for Building Twitter Corpora of Smaller Languages[END_REF]. Fourth, geocoding algorithms can be used to help recreate absent geolocation metadata, using textual mentions of place [START_REF] Leetaru | Mapping the global Twitter heartbeat: The geography of Twitter[END_REF] or linguistic cues [START_REF] Scheffler | Mapping German Tweets to Geographic Regions[END_REF] based on the identification of "local words" [START_REF] Cheng | You are where you tweet: a content-based approach to geo-locating Twitter users[END_REF]. On the one hand, there are potential ambiguities in place names that have to be resolved to establish a reliable list of Austrian places, which implies a significant amount of work with an unknown outcome. On the other hand, the tweets are not exclusively in German and I do not agree with the segmentation of Austria in one bloc as used by [START_REF] Scheffler | Mapping German Tweets to Geographic Regions[END_REF]. That is why no attempt is undertaken to recreate location metadata. Finally, so-called "heavy tweeters" [START_REF] Krishnamurthy | A Few Chirps about Twitter[END_REF] as well as peculiarities of the API [START_REF] Morstatter | Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose[END_REF] raise the question of sampling processes. Although human users usually entertain a stable amount of stable relationships [START_REF] Gonc ¸alves | Modeling users' activity on Twitter networks: Validation of dunbar's number[END_REF], it is conceivable that heavy users as well as machine-generated tweets account for distortions in the corpus. Additionally, the random sampling methodology used by Twitter to generate the streams of tweets is rarely put into question [START_REF] Zafar | Sampling content from online social networks: Comparing random vs. expert sampling of the twitter stream[END_REF]. This means that steps have to be taken in order to minimize the impact of differences in user activity as well as potentially unknown sampling biases.

Implementation

To sum up the methodological concerns, what is needed is a method allowing to find and collect tweets from Austrian users with a reasonable precision. My method uses different modules as presented in figure 1. The first component can be considered to be a "lurker" module in the sense that it merely listens to the Twitter's streaming API1 to collect geolocated tweets whose coordinates are in or close to Austria. Tweets featuring geolocation in Austria or with a user profile location field linked to Austria are singled out. The corresponding user names are then passed to a second module which fetches user streams in order to analyze them. Additionally, the social networks (friends and followers) are crawled [START_REF] Kumar | Twitter Data Analytics[END_REF] in order to find other potentially interesting users, which makes the operation comparable to an API-side focused or scoped crawling [START_REF] Olston | Web Crawling[END_REF]. The communication with the API relies on the Python wrapper twython. 2The constant filtering is meant to optimize the collection. In fact, there are mechanical constraints on both ends: access to the API on user level is limited to 180 requests per slot of 15 minutes, and on the other side unneeded content may clutter up storage devices. Additionally, I found that potentially interesting users are geographically and linguistically very mobile; they may use several languages and be tied to several home places. Finally, even among users who use geolocation services, the proportion of tweets with actual location data may greatly vary, so that users are unequally productive in this respect. Studies have shown that it is desirable to gather a set of users which is both large and diverse [START_REF] Zafar | Sampling content from online social networks: Comparing random vs. expert sampling of the twitter stream[END_REF], so that the collection process is opportunistic despite a rather conservative setting concerning location: at least 50% of geolocated tweets per user have to be in Austria. Positives in the user location field are found on token level using a fixed list of case-insensitive cues: nationwide mentions (e.g. Austria), all regions (Bundesländer), well-known landscapes (e.g. Waldviertel), and top-20 cities. For the sake of completeness, the main quarters of the major cities (e.g. Josefstadt in Vienna) as well as major geographical features (e.g. valleys and rivers) have been added, however they seem to be rarely used. Quantitatively speaking, the number of users found that way (around 125,000) is concordant with results from market studies, with an estimated number of 140,800 Austrian users in September 2015. 3The corpus is constantly growing, and so is the user base. Filtering steps include the deduplication of tweets and the blacklisting of unwanted users, which both yield statistical information for quality assessment. At the same time, remaining tweets are scanned for other user names in replies or retweets, whose timelines are retrieved and stored if they match the location criteria. In order to avoid bias by heavy twitterers, the timelines are fetched at random intervals among the range of valid users.

Indexing and results

To keep up with the growing amount of tweets, a specific search engine has been chosen. The interest of NoSQL databases to deal with the feature-rich content return by the Twitter API is known [START_REF] Kumar | Twitter Data Analytics[END_REF]. Two main components of the open-source ELK stack (Elasticsearch, Logstash, Kibana) are used, namely Elasticsearch4 to index the tweets and Kibana5 to provide a user-friendly interface to queries, results, and visualizations. The main drawbacks result at the time being from the lack of linguistic processing: a rather unprecise lemmatization of queries and results by the search engine as well as a lack of linguistic annotation. These tasks will require a substantial amount of testing due to the multiple languages and the difficulty of twitter messages. Although it is not primarily a search engine for linguists, Elasticsearch takes advantage of the native JSON format of the tweets as well as of a number of relevant field types after a subsequent mapping, which allows for refined queries on text and metadata, for instance "the -erl diminutive form in tweets from users with more than 10 followers and with the city of Klagenfurt mentioned in the home location field". In the current implementation, using Kibana's syntax, this query translates to text:*erl AND user.followers count:[10 TO *] AND user.location:Klagenfurt. In order to give a user-friendly access to the results, dashboards can be configurated out of a series of indicators (see figure 2). The most frequent languages according to the metadata delivered by Twitter are English (42.2% of all tweets) and German (40.5%), with a number of less frequently represented languages such as Turkish (2.8%), Spanish (1.3%), and Japanese (0.9%). The amount of tweets whose language could not be determined by Twitter is relatively low (6.5%), which indirectly yields insights on the quality of the corpus. This information is confirmed by the mean length of the tweets (100.4 characters and 12.7 tokens).

The proportion of geolocated tweets (3.3%) is better than in the comparable German Snapshot [START_REF] Scheffler | A German Twitter Snapshot[END_REF], where it amounts to 1.1%. Their distribution by country is largely in favor of Austria (75.0% of geolocated tweets), with a number of other less prominent countries such as the USA (6.2%), Germany (4.1%), and Turkey (1.6%). These figures show that it is necessary to target Austria in comparison to a general approach targeting German. Visualizations of geographical data can be constructed "out of the box" as soon as coordinates have been mapped as geographical data in the database, which allows for the projection of geolocated tweets on a map. A heat map centered on Austria is shown in figure 3. The distribution of tweets is mostly in line with population distribution, with the exception of Klagenfurt. It highlights the prominence of Vienna and its airport as well as the importance of commuters and travellers, with train tracks partially visible. Holiday resorts such as ski stations are also depicted on the map, which altogether prompts for geographical and sociological analyses of mobility.

Conclusion

I introduced a monitor corpus of tweets from Austrian users. The data collection grounds on a two-tier detection process addressing corpus construction and user location issues. The emphasis lies on short messages whose sender (1) mentions a place in Austria as his/her hometown or (2) often tweets from places located in Austria. The resulting user base is then queried and enlarged using random sampling. The current volume of the corpus is 21.7 million tweets from approximately 125,000 users, which is roughly comparable to the German Snapshot [START_REF] Scheffler | A German Twitter Snapshot[END_REF] in terms of volume with a number of users one order of magnitude smaller. The tweets are mainly written in English and German. The proportion of geolocated tweets is 3.3%, 75.0% of which come from Austria. Future work includes work on fine-grained differences in geolocations which could improve the quantitative throughput as well as the qualitative value of the corpus. In the same perspective, ambiguities of gazetteers have to be reduced to a minimum in order to use them in the user selection process, as the corpus collection will be extended to Germany and Switzerland. Further, user names could be used in order to improve filtering and get insights on distributions of language and gender in the corpus [START_REF] Jaech | What Your Username Says About You[END_REF]. Last, tweet identifiers can allow for reuse of the corpus [START_REF] Mccreadie | On Building a Reusable Twitter Corpus[END_REF] which could also be done with user identifiers.

Figure 1 :

 1 Figure 1: Schema of the implementation

Figure 2 :

 2 Figure 2: Example of dashboard view

Figure 3 :

 3 Figure 3: Heat map of all geolocated tweets

https://dev.twitter.com/streaming/overview

https://github.com/ryanmcgrath/twython

http://de.statista.com/statistik/daten/studie/296135/ umfrage/twitter-nutzer-in-oesterreich/

https://www.elastic.co/products/elasticsearch

https://www.elastic.co/products/kibana


Introduction

This brief introduction to the Kunming dialect is intended to help the reader begin to make the adjustments from standard Mandarin -普 通 话 . We assume familiarity with standard Mandarin. We have made no attempt to follow any rigorous theory of phonology, but desire to be simply descriptive. There are several other sources one can consult (cf. Bibliography), but the one we referred to most -not including our own study -was Gui Mingchao's 1990 dissertation. Gui discusses differences between the old Kunming dialect and the modern one. For pedagogical reasons we will note areas of difference, but we will basically limit our discussion to modern Kunminghua.

It might be helpful for the reader to realize that Kunminghua (hereafter KMH) is not a mutated form of modern Mandarin (hereafter PTH), but both KMH and PTH are modern dialects of an older Mandarin. In fact, certain characteristics of this older Mandarin have been preserved in KMH, but lost in PTH. Many of the differences between PTH and KMH are consistent throughout Southwest China. For example, the province 湖南 is pronounced hu²nan² in PTH, but is pronounced fu²lan² throughout much of Southwest China. As such, it is our hope that this description of KMH can be used as a springboard into the study of other Southwest dialects.

Kunming, like many places in China, is a very diverse linguistic environment. The Chinese spoken throughout the province differs from area to area, having been influenced by the minority languages spoken there. Of course, many of these country people can be found in Kunming. Kunming also has people from North China who speak clear Mandarin, and people from Guangdong, Shanghai and other non-Mandarin areas. In the markets one will find that there are many people from August, 2008 Version Sichuan. In fact, a high percentage of the peddlers (e.g. shoe repairmen, bike repairmen, etc.) are from Sichuan. And as expected, a person's age, educational level and exposure to other dialects of Chinese greatly affects their spoken language. The obvious question is "What is standard KMH?" We have tried to gather data from native Kunming people. In spite of this, we have found there is a fairly wide range of sounds that are produced for the same words. We have attempted to follow the most conservative path, excluding extremes on either end. For example, sometimes we found that there were consistent patterns, but then a speaker would articulate a word with PTH pronunciation and KMH tones. We usually didn't include examples like these in the range of KMH.

The phonetic script used is IPA, but we chose the symbols used in China where standard IPA was lacking (i.e. the apical vowels [ɿ] and [ʅ]). We have used superscript numerals to represent the tone pitch (e.g. ma⁵⁵ is high and ma¹¹ is low). Contour tones are represented with two different numbers juxtaposed (e.g. high-rising ma³⁵).

We would like to express our appreciation to Ms. Hannah Yang (杨红玉), Mr. John Zhang (张镇华) and Mr. Zhao Tianpei (赵天培) for their help in supplying us with the majority of our data. We are also thankful to Bryan and Silvia Allen, Dottie Martin and Lon Diehl for their helpful feedback. We hope this small description of KMH proves helpful to others. If the reader has suggestions for improvements in the presentation or comments for correction, please let us know.

Initials in Kunming Hua

Gui gives part of the following list of initials for modern KMH. He points out that some of the old people still retain some evidence of the retroflexed consonants, but that younger

n f v s z (ʂ ʐ) ɕ X w l j
Moreover, we have noticed some speakers pronounce the retroflexed set as palato-alveolar (tʃ, tʃʰ, ʃ, ʒ). These same speakers will also pronounce the same words or homophones with the alveolar set. For consistency we will transcribe these all as alveolars, but the reader should be aware of these variations.

In KMH there are several deviations in initials from PTH. One of these we have already pointed out, namely the fronting of the retroflexed initials. The following examples from [START_REF] Gui | The phonology of Yunnanese and Kunming Chinese: a comparative study[END_REF] show that in modern KMH the retroflexed consonants have been lost.

忘记

However, the initial of the following words from [START_REF] Gui | The phonology of Yunnanese and Kunming Chinese: a comparative study[END_REF]

Finals in Kunming Hua

Mandarin has more than 30 combinations of finals. In KMH some of these do not vary from PTH, but many of them vary in more than one way. The following are the possible combinations of finals in PTH written using Pinyin. a, ia, ua, ao, iao, ie, u, ou, i, o, uo, e, ü, üe, üan, ün, iu, ai, uai, ei, ui, an, ian, uan, en, in, un, ing, eng, ang, iang, uang, ong, iong The finals that are not different from Mandarin, or where the difference could be attributed to the local way of pronouncing the same segment, are as follows: a, ia, ua, ao, iao, ie, u, ou, i.

The remaining we will now discuss.

August, 2008 Version

Finals o, uo [START_REF] Cheng | A synchronic phonology of Mandarin Chinese[END_REF] gives the phonetic form of these pinyin finals as [wo]. This final may be used with all the KMH initials except [v], and the alveolo-palatal set, [tɕ, tɕʰ, ɕ]. There is one syllable without a consonantal initial: [wo], also wo in pinyin.

Within this group we elicited examples from a wider selection of people than for some of the other finals, because we found that it was difficult to pin down a clear-cut pattern. We believe the standard KMH pronunciation for [wo]

法律

One exception that we found to this is the following: lu³¹²sɣ³¹² green 绿色 One hypothesis we have is that whenever there is an alternate older Mandarin (proto-Mandarin?) pronunciation for a given character (as also seen in PTH), KMH chooses the phonologically less marked one. Thus [lu] [START_REF] Cheng | A synchronic phonology of Mandarin Chinese[END_REF] gives the phonetic form [jow] for the pinyin iu. In KMH this is simply [u]. However, we found that often words with this final were pronounced very similarly to PTH. Consider the following examples that display the difference:

tɕu⁵⁵ nine 九 lu³¹² six 六 nu⁴² cow 牛 Finals ai, uai, ei
The phonetic forms of these three finals in PTH are the same as the pinyin forms. We have grouped these three together because, first, ai [aj] and uai [waj]

没的

Recall that earlier we said that ai is also sometimes said as [ɐ].

3 This difference apparently only applies to the alveolar sonorants. The syllable dei³ 得 'must' is not included here because KMH chooses the less marked pronunciation of [tɣ⁵³]. The syllables cei and tei don't exist. And the syllable zei² 贼 'wicked' is the same as PTH. Final: ui [START_REF] Cheng | A synchronic phonology of Mandarin Chinese[END_REF] gives the phonetic form [wej] for the pinyin ui. This corresponds to [wi] in KMH. Most of the nasal finals behave similarly. Basically, the nasal consonant is deleted and the vowel is pronounced as a nasal. This is true with all the nasals listed here and below, but not including the ong, iong set.

August, 2008 Version

Within the set of finals that close the syllable with the alveolar [n] there are some differences. With certain vowels the [n] always deletes (viz. an, ian, uan), but with others it appears to be somewhat optional (viz. en, in, un).

August, 2008 Version

The finals in this group and the following set are the entire velar nasal consonant group. These differ from the previous set in that the velar nasal consonant is never articulated. If there is a nasal consonant it is an alveolar [n] or bilabial [m] (cf. next set). If there is no nasal consonant the vowel, as would be expected, is then nasalized. Similar to the previous set of finals, the finals ing [iŋ] and eng [əŋ] more often do appear as [in]

壮族 Finals ong, iong

There is usually a nasal consonant articulated with these finals, but it is always bilabial [m], a sound that PTH does not use in the syllable final position. Consider the following:

Tones in Kunming Hua

Tones in KMH are quite interesting. It's been commented by some that there seems to be no real system. Others have said that there is only one tone: falling. These observations, of course, are not accurate, but we have elicited words that carry the first tone in PTH and are pronounced with a clear falling tone in KMH.

For example, the word 掐 qia¹ 'to pinch, throttle', was pronounced with approximately a [42] falling pitch in isolation.

Phenomena like this would certainly lead one to make initial observations like the above. [START_REF] Gui | The phonology of Yunnanese and Kunming Chinese: a comparative study[END_REF] There are three differences between our list and Gui's. The reason for the first difference can be seen by comparing Tone 1 with Tone 3. Tone 3 is always higher pitched than Tone 1, therefore we have called Tone 1 a 33 pitch and Tone 3 a 55 pitch. The second and third differences have to do with Tone 2 and Tone 4. Tone 4 is pitched very low, but it always has the contour of low falling (sometimes with a slight rise). Tone 2 actually starts higher than Tone 1 and doesn't go as low as Tone 4. Although this is the phonetic shape of Tone 2, we would still call it low falling, and call Tone 4 low level. We will discuss this below.

When one listens to spoken KMH, in contrast to PTH, it sounds very low and laryngealized. At times it sounds like the speakers are growling at each other. The reason for this impression is probably two-fold. One is that Tone 1 is pronounced as a mid-level tone in KMH. The other reason is that Tone 4 is pronounced with what is known as creaky or laryngeal voice. It is quite plausible that this tone is simply low level phonemically, like Tone 3 in PTH. The reason for the contour might be that it is difficult to pronounce such a low tone without a slight fall (cf. [START_REF] Yip | The tonal phonology of Chinese[END_REF].

August, 2008 Version

The creaky voice of Tone 4 helps distinguish it from Tone 2. It is this tone that gives KMH its characteristic sound. Probably the creaky voice is due to the fact that its pitch is very low. This laryngealization can be exaggerated to the point of a glottal stop being inserted in the middle of the syllable rime. For example: ku⁵⁵taeʔae³¹² ancient times

古代 xə5⁵taʔa³¹²

very big

很大

The phonetic shape of Tone 3 can sometimes be pronounced similarly to the Mandarin Tone 4 (i.e. 51 high-falling). This usually occurs when a word is said in isolation or when Tone 3 is utterance final. Usually, though, it is pronounced as 53 highfalling in this environment. [START_REF] Gui | The phonology of Yunnanese and Kunming Chinese: a comparative study[END_REF]

Tone 1 Sandhi

Tone 3 Sandhi

Another tone sandhi rule for KMH is Tone 3 changing from (55) high-level to (53) high-falling. The environment for this change, as mentioned above, is prepausal or utterance final. This change also sometimes occurs before another syllable that is toneless (i.e. a neutral tone). This analysis is different from what one will find in the literature. The assumption is a character pronounced in isolation rather than in context is more basic. We reject this since a word in isolation is both utterance initial and final, which is an unnatural environment. We consider a word said in the middle of an utterance to be more natural and thus the more basic form. Andy Eatough has pointed out that certain Tone 3 words in Chengdu Hua have a falling tone. These words historically, he notes, were entering tone (入 声) words. The word 'north' 北 is one of these.

August, 2008 Version

考试

There are examples like the following where the second syllable carries the neutral tone, but there is no sandhi besides the neutralizing of the second syllable's tone: tɕjɛ⁵⁵tɕjɛ⁵⁵ --> tɕjɛ⁵⁵tɕjɛ³³ older sister 姐姐 But there are also examples of a Tone 3 syllable preceding a neutral tone syllable with the sandhi.

sɿ⁵⁵lə³³ --> sɿ⁵³lə³³ died 死了 [START_REF] Yip | The tonal phonology of Chinese[END_REF] notes this same phenomenon with Tone 3 sandhi in PTH, namely inconsistent application of the tone sandhi before neutral tones. The Tone 3 sandhi rule doesn't apply in the 'older sister' example, because the deletion of the tone on the second syllable occurs after the sandhi rule. In the second example it is the case that the le 了 syllable has no tone to begin with so the sandhi rule can apply.

A good example of Tone 3 syllables in context is the wellknown sentence: "Old Lee buys good wine." It can be seen in this example that four of the five syllables are pronounced with the high-level tone. Only the last syllable is pronounced with a falling tone, and this is because it is utterance final.

lɑw⁵⁵li⁵⁵ mae⁵⁵ xɑw⁵⁵ tɕu⁵¹ 老 李 买 好 酒

August, 2008 Version [START_REF] Gui | The phonology of Yunnanese and Kunming Chinese: a comparative study[END_REF] [START_REF] Gui | The phonology of Yunnanese and Kunming Chinese: a comparative study[END_REF] also points out that reduplicated verbs can have a neutral tone second syllable like PTH.

kʰã³¹²kʰã³¹² --> kʰã³¹²kʰã³³

to look 看看

It is important to note that the above example with 'fruit' is somewhat of an exception because the noun suffix [-tsɿ] (子) often carries Tone 3. As seen above in the example of "Old Lee buys good wine," when Tone 3 is utterance final its phonetic shape can also be pitch [51].

kəw³³tsɿ⁵⁵ --> kəw³⁵tsɿ⁵¹ hook 钩子 xəw⁴²tsɿ⁵⁵ --> xəw⁴²tsɿ⁵¹ monkey 猴子 pej³¹²tsɿ⁵⁵ --> pej³¹²tsɿ⁵¹ quilt 被子

Intonation

We will not discuss this topic in detail, but we want to point out that KMH has a strong tendency towards falling intonation. For example, a two syllable Tone 4 word will display a lower Tone 4 on the second syllable. But this tendency is also seen at the sentence level. If a sentence contains syllables with the same tone, one occurring early and the other late, the later syllable will display a lower pitch of the same contour. Irregularities like these make it difficult to find patterns when one listens to KMH as an outsider. We discuss these differences here because they seem to be motivated lexically rather than phonologically. There are many other clear-cut lexical differences between KMH and PTH. Many of them do not seem to have a corresponding character. When one asks a local person to write down a word, they either write down the character with that meaning or say that there is not one. Following the lead of Teacher Zhao (赵天培), we will give what we consider to be the most suitable character for a given word. Consider the following:

你家请点哪样?(= 您想吃一点什么?) tsae³¹² tɕʰĩ⁵⁵ ji³¹²tjɛ5³

Have some more to eat!

再请一点!(= 多吃一点!) kae⁴² tɕʰĩ⁵³ tə³³ fã³¹²lə³³

Have you eaten?

咯请的饭了?(= 吃饭了吗?) tɕʰĩ⁵⁵ tsʰɑ⁴²

Please drink some tea.

请茶! tɕʰĩ⁵⁵ tɕu⁵³

Please drink some wine.

请酒!

When the word 请 means something other than 'to eat' or 'to drink', it is used as in the following examples: mã³¹² tɕʰĩ⁵³ (sending off guest, a polite statement)

慢请! tɕʰĩ⁵⁵ lju⁴²pu³¹²

(guest to host on leaving: 'Don't bother sending me.') 请留步。 KMH uses the particle [kae⁴²/ kɣ⁴²] 咯 extensively. It is basically a question word like ma 吗, but it is also more than that. It is an interjection with several possible readings. When used as a question word it can be placed before the verb or at the end of the sentence.

咯是?(= 对吧?)

KMH has many set expressions that make sense once the meaning has been explained, but might not be apparent when the language student first hears them.

2

  is [o]. Some people's pronunciation was quite close to PTH. Other people articulated [wo] with certain initials (i.e. the velars) and [o] with the rest of the initials, while a few other people said [wə] for many words. A few people even said [ɔw]. We give the following examples of what we believe to be standard KMH. There are two pronunciations of the pinyin final e, namely, [ə] as in le (了) and [ɣ] as in gege (哥哥). The [ə] in le is often said as [a], producing [la]. This seems to occur when there is stress on August, 2008 Version the particle le (了). The vowel is drawn out in length. For example: xwae³¹²lə³³ --> xwae³¹²laː³³ ruined 坏了 The PTH [ɣ] is equivalent to [o] in KMH following the velar initials (k, kʰ, x). Following all other initials it is the same in KMH as in PTH. The set of high, front, rounded finals follow a fairly consistent pattern. There are exceptions to this that might be lexical rather than phonological, but the rule is basically the PTH ü [y] is pronounced [i] in KMH. Consider the following examples: ü [y] : KMH [i] There are exceptions to this as well. We have elicited examples of le ( 快 乐 ) and me ( 什 么 ) where the final was pronounced as [o] instead of [ɣ] or [ə]. August, 2008 Version fa⁵⁵li³¹² law

甩内

  These data look fairly consistent. We examine the exceptions below, comparing them to some examples of ei[ej]. August, 2008 Version The changes of the final ei [ej] are not as neat. When the final ei is preceded by the alveolars [n] and [l] it is pronounced as [When the final ei follows [m] or [f] it can be pronounced as [ɛ] but not consistently. It can be articulated anywhere from the PTH [ej] to [ɜ], a lower-mid vowel. mɛ⁵⁵ko⁴² USA 美国 fɛ³⁵fu⁵⁵ The Flying Tigers 飞虎 The last two possible pronunciations of the final ei are [ɣ] and [ɐ]. Consider the following examples:

  variations the words 'north' 北 and 'hundred' 百 are homophones in KMH: [pɐ⁵⁵].

  discusses the change of Tone 1 (pitch = 33) changing to high-rising (35) preceding any tone except Tone 1. Consider the following: Tone 1 preceding Tone 2 xwa³³tsʰa⁴² --> xwa³⁵tsʰa⁴² flower-tea 花茶 səw³³tɕi⁴² -jəw³⁵ɕju³¹² excellent 优秀 wã³³təw³¹² --> wã³⁵təw³¹² pea 豌豆 It can be seen that in the above examples Tone 1 changes from 33 mid-level to 35 high-rising before Tones 2,3, and 4. When Tone 1 precedes another Tone 1 there is no change.

5

 5 We found at least one exception to this rule. The word 'Beijing' 北 京 is pronounced as [pɐ 53 tɕĩ 33 ].

  kʰaw⁵⁵sɿ³¹² test

  The word 'please' 请 has several meanings in KMH. It is really a term of politeness. It can mean 'eat', 'drink', 'please', etc. Consider the following: What would you (hon.) like to eat?

  know, or can't recognize 认不得!(= 不知道、认不出) tsən⁵³pu³³ tsʰən⁴² can't do it, or no can do 整不成!(= 搞不成、不行) A word that is heavily used in KMH is [tsɑ³¹²kɣ³³] 咋个 'how'. Consider the following examples: how much do the shoes cost? 鞋子咋个卖?(= 鞋子多少钱?)

  August, 2008 Version speakers by and large have lost this distinction. One thing that we have noticed is that certain speakers actually switch the alveolar set with the retroflexed set, pronouncing 四, sɿ³¹², as ʂʅ³¹².

	Bilabial Labio-	Alveolar Retroflex Alveolo-	Velar
	dental		palatal
	p pʰ	t	tʰ	k kʰ
		ts tsʰ (tʂ tʂʰ) tɕ tɕʰ
	m

  At least three exceptions to this pattern are the words 'to study' 学习 [ɕju⁴²ɕi⁴²], 'to plunder' 掠 夺 [lju³¹²to⁴²] and 'brief, sketchy' 略 [lju³¹²].

	pʰɐj⁴²ɕin³¹²	cultivate	培训
	jin⁴²nã⁴²	Yunnan	云南
	Final: iu
		is less marked than
	[ly].
	üe [yɛ] : KMH [jɛ]
	tɕjɛ⁴²tĩ³¹²	to decide	决定
	tɕʰjɛ³⁵saw⁵⁵	to lack	缺少
	ɕjɛ⁵⁵
	üan [yɛn] : KMH [jɛ]
	tɕjɛ5⁵	roll	卷
	tɕʰjɛ4²	whole	全
	ɕjɛ5⁵tsə⁴²	to choose	选择
	jɛ5⁵
		the masses	群众
		August, 2008 Version

snow 雪 jɛ³¹²fən³¹² month 月份 distant 远 ün [yn] : KMH [in] tɕin³⁵twi³¹² army 军队 tɕʰin⁴²tsom³¹²

  are basically the same, and second, the pronunciation ai [aj] and ei [ej] converge in KMH for certain initials. It is difficult to give a simple rule for pronunciation of these finals. For most cases PTH's ai [aj] correlates to KMH's [ae] and uai [waj] is equivalent to [wae]. But there are cases where ai [aj] is articulated as [ɣ] and sometimes [ɐ], a low-central unrounded vowel. This is where it starts to get difficult, as PTH's ei [ej] is also pronounced [ɐ] in certain environments but [ɛ] in others, and yet [wej] in others. First consider ai [aj] and uai [waj].

	be defeated	败
	August, 2008 Version

ai [aj] : KMH [ae] ʔae³¹² love 爱 pae³¹²

  The finals en[ən], in[in] and un [wən] can follow the pattern described above of deleting the nasal consonant and nasalizing

	an [an] : KMH [ã] the vowel, but these finals are more often pronounced with the
	ʔã³³ syllable final [n]. 4	peaceful	安
	pã³³ en [ən] : KMH [ən] or [ə] class, team	班
	tsʰã³³tɕja³³ kʰo³¹²pən⁵⁵ / kʰo³¹²pə5⁵ textbook to participate	参加 课本
	fã³¹² fən³³ / fə3³	rice	to divide, part	饭 分
	kã⁵⁵ zən⁵⁵ / zə5⁵	to feel to endure	感 忍
	nã⁴² sən³⁵lin⁴² / sə3⁵lĩ⁴²	difficult forest	难 森林
	sã³³ tʰã⁴²xwa³¹² in [in] : KMH [in] or [ĩ] mountain to talk lin⁴²tɕi³³ / lĩ⁴²tɕi³³ neighbor	山 谈话 邻居
	ien [jɛn] : KMH [jɛ] pʰin³³jin³³ / pʰĩ³³jĩ³³	pinyin	拼音
	pʰã⁴²pjɛ3³ zəñ⁴²min⁴² / zə4²mĩ⁴²	side the people	旁边 人民
	tsʰɿ⁴²tjɛ5⁵ ɕin³³ / ɕĩ³³	dictionary new	词典 新
	sɿ⁴²tɕjɛ3³ ljɛ5⁵ un [wən] : KMH [wən] or [wə] time face jɛ3³ smoke writing wən⁴² / wə4² tsʰwən⁴²tsə3¹² / tsʰwə4²tsə3¹² exist	时间 脸 文 烟 存在
	uan [wan] : KMH [wã] twən³³ / twə3³	squat on heels 蹲
	wã³¹² kʰwən³⁵mĩ⁴² / kʰwə3⁵mĩ⁴² ten thousand Kunming	万 昆明
	tsʰwã³³ Finals ing, eng, ang, iang, uang to wear	穿
	kwã³⁵ɕi³¹²
	4 We elicited the word 'tender' nen⁴ 嫩 , and
	found it to be an exception in an unexpected way.
	It was pronounced [nwən³¹²], resembling [nei] in that
	a [w] is epenthasized.
	August, 2008 Version August, 2008 Version

relationship 关系 nwã⁵⁵xo³³ warm 暖和 zwã⁵⁵ soft 软

  and [ən].

	iang [jaŋ] : KMH [jã]
	tɕjã⁵⁵xwa³¹²	to talk	讲话
	ljã⁴²xaw⁵⁵	good	良好
	tɕʰjã⁴²	strong	强
	ɕjã⁵⁵	to think	想
	uang [waŋ] : KMH [wã]
	xwã⁴²ti³¹² ing [iŋ] : KMH [in] or [ĩ] emperor ice kʰwã⁴²tɕjɛ5⁵pĩ³¹² rabies pin³³ / pĩ³³ nail swã³³ pair tin³⁵tsɿ⁵⁵ / tĩ³⁵tsɿ⁵⁵ tɕin⁵⁵ / tɕĩ⁵⁵ well tswã³¹²tsu⁴² Zhuang Nationality	皇帝 冰 狂犬病 钉子 双 井
	nã⁴²lin⁴² / nã⁴²lĩ⁴²	Nanning	南宁
	eng [əŋ] : KMH [ən] or [ə]
	tsʰən⁴² / tsʰə4²	layer, stratum	层
	fən³³ / fə3³	wind	风
	kən³¹² / kə3¹²	even more	更
	nən⁴² / lə4²	able	能
	The finals ang, iang, uang [aŋ, jaŋ, waŋ] never surface with a
	nasal consonant.
	ang [aŋ] : KMH [ã]
	pã³⁵tsu³¹²	to help	帮助
	ɕjã³³tã³³	to be equal to	相当
	fã³³	square	方
	kã³⁵pi⁵⁵	fountain pen	钢笔
	August, 2008 Version

  gives the following pitch values for KMH (the PTH values are from Yip 1990):

	But based on our own acoustical studies we would claim that the
	tones in modern KMH are as follows:
	Category	KMH	Allotone
	Tone 1	33	35
	Tone 2	42
	Tone 3	55	53
	Tone 4	312
	Category	PTH Value KMH Value
	Tone 1	55	44
	Tone 2	35	31
	Tone 3	214	53
	Tone 4	51	212
	Gui also points out that another complicating factor in KMH is
	there are some differences between the speech of older and
	younger speakers. He gives the following tonal inventory for
	older speakers of KMH:
	Category	Old KMH
	Tone 1	44
	Tone 2	33
	Tone 3	53
	Tone 4	11
		August, 2008 Version

  also gives some examples where the tone of 头 [tʰəw] doesn't neutralize. In these cases the syllable [tʰəw] is stressed and it seems to still carry its primary semantic meaning of 'head'. Moreover, these words in PTH do not neutralize the syllable[tʰəw].

	kəw⁵⁵tʰəw⁴²	dog's head	狗头
	səw⁵⁵tʰəw⁴²	at hand		手头
	In KMH the word 首 [səw] is also used as a suffix the way 头
	[tʰəw] is used. As a suffix it is not stressed and its tone is
	neutralized.
	ɕja³¹²səw⁵⁵ --> ɕja³¹²səw³³ below	下首
	kaw³³səw⁵⁵ --> kaw³³səw³³ above	高首
	tɕʰjɛ4²səw⁵⁵ --> tɕʰjɛ4²səw³³ front	前首
	Other examples of a neutral tone second syllable are given
	below.
	ja⁴²tsʰɿ⁵⁵ --> ja⁴²tsʰɿ³³	tooth	牙齿
	ko⁵⁵tsɿ⁵⁵ --> ko⁵⁵tsɿ³³	fruit	果子
	ɕjɛ³¹²ɕjɛ³¹² --> ɕjɛ³¹²ɕjɛ³³	thanks	谢谢
	Now consider the following three examples with the neutral tone
	particle le 了:
	pɐ⁴²lə³³pɐ⁴²	extremely white	白了白
	lã⁴²lə³³lã⁴²	extremely blue	蓝了蓝
	tsʰɿ³³la³³	have eaten		吃了
		August, 2008 Version

August, 2008 Version

Neutral Tone

Unlike PTH the neutral tone in KMH consistently has the pitch value of mid-level (i.e. 33). 6 It looks like Tone 1 except it is usually in the context where a neutral tone would be expected (i.e. unstressed syllables). For example, when a familial title is reduplicated the second syllable loses its original tone. Consider the following: ti³¹²ti³¹² --> ti³¹²ti³³ younger brother 弟弟 pə⁴²pə⁴² --> pə⁴²pə³³ uncle (father's older bro.) 伯伯 tɕjɛ⁵⁵tɕjɛ⁵⁵ --> tɕjɛ⁵⁵tɕjɛ³³ older sister 姐姐 pʰo⁴²pʰo⁴² --> pʰo⁴²pʰo³³ mother-in-law 婆婆

In PTH when a noun suffix like 头 [tʰəw] is affixed to a word it is usually articulated with the neutral tone. This is also true in KMH as can be seen in the following examples from Gui 1990 7 :

6 [START_REF] Gui | The phonology of Yunnanese and Kunming Chinese: a comparative study[END_REF] has several spurious tone sandhi rules which all can be eliminated by the recognition of the neutral tone. The "Yunnan Survery, vol. 58 of the Chinese Dialect Survey" 1989:134 (i.e. 云 南 省 志 , 卷 五 十 八 , 汉 语 方 言 志 ) also points out that the KMH neutral tone is mid-level.

7 We have standardized Gui's tones to our system.

August, 2008 Version

The drift is downward. The syllable [tsɑ³¹²] and [mae³¹²] are both Tone 4 words, but [mae³¹²] is much lower in actual pitch than [tsɑ³¹²]. Also, the syllable [kɣ³³], which carries a neutral tone, is much lower in pitch than [nə³³], which also has a neutral tone. It is examples like these that lead us to say that KMH's intonation is falling.

Lexical differences in Kunming Hua

Perhaps the most difficult differences between PTH and KMH for foreign students are lexical. These differences are often just shrugged at and called 'dialectical', 方言, as if this makes it easier or less important. But the fact is, when a student of PTH first hears the question [ni⁵⁵ kʰɣ³¹² na⁵⁵tjə5³] they have no idea that it means 'Where are you going?' 你 去 哪 儿 ? The following list is by no means exhaustive, but we hope it proves helpful in becoming acquainted with this 'dialect'.

We mentioned above that there are exceptions to the phonological changes and that we propose it might be due to the fact that a given character had more than one pronunciation in proto-Mandarin. KMH chooses the lesser-marked option. We have sighted examples like 'must' 得 and 'green' 绿色. Now consider the following examples:

The pronunciation of 'medicine' is [jaw] in PTH. The first syllable of 'appointment' is pronounced [yɛ] in PTH. An alternate pronunciation for the character 约 is [jaw]. Therefore, it would seem that this alternate pronunciation has been chosen in KMH since it is articulated like the word 'medicine'. August, 2008 Version The last domain of differences that we would like to point out is words dealing with time. In KMH the word [tsən³¹²] 阵 is used to mean 'time, period of time'.

这阵 (= 现在、这时候)

There are many more expressions that are used in KMH that we could list here. Some are 'common sayings' 俗语 and some are similar to those above. There are also terms of relationship that differ from PTH. In short, our list is simply a start. We hope that it will help in understanding this dialect of Mandarin as the language student lives and works in Kunming.

August, 2008 Version

Zhang Huawen and Mao Yuling 张华文、毛玉玲。1997. 昆 明方言词典,Kunming Fangyan Cidian (A dictionary of the Kunming dialect). Kunming: 云南教育出版社。