The spatiotemporal forming of a state of exception: repurposing hot-spot analysis to map bare-life in Southern Arizona’s borderlands

Item Type Article

Authors Chambers, Samuel Norton

Citation Chambers, S.N. The spatiotemporal forming of a state of exception: repurposing hot-spot analysis to map bare-life in Southern Arizona’s borderlands. GeoJournal 85, 1373–1384 (2020). https://doi.org/10.1007/s10708-019-10027-z

DOI 10.1007/s10708-019-10027-z

Publisher SPRINGER

Journal GEOJOURNAL

Rights Copyright © Springer Nature B.V. 2019.

Download date 25/09/2021 18:38:00

Item License http://rightsstatements.org/vocab/InC/1.0/

Version Final accepted manuscript

Link to Item http://hdl.handle.net/10150/648100 Manuscript

1 2 3 4 The Spatiotemporal Forming of a State of Exception: Repurposing Hot-Spot Analysis to Map Bare- 5 Life in Southern Arizona’s Borderlands 6 7 8 Abstract 9 Through the use of Hot-Spot analysis, typically reserved for local analysis of crime and law enforcement, 10 11 I document the dispersal and clustering of migrant mortalities on a temporal scale in the Ajo valley of 12 Southern Arizona in the U.S.-Mexico borderlands. The study maps the influence of border enforcement 13 by time and documents the forming of a state of exception, by finding whether and where migrants had 14 taken other more-remote routes in relation to the constructing of and policing by a Border Patrol 15 16 checkpoint, The spatiotemporal nature of ‘Hot’ and ‘Cold-Spots’ plus an analysis of migrant mortality 17 locations before and after the establishment of a checkpoint serves as a novel approach to spatial analysis 18 in border studies. It creates a type of remote forensics for verifying the ‘funnel effect’ and the condition of 19 20 Bare Life it produces where law has taken migrant’s political power and left them with their biological 21 existence (Agamben, 1998). I show a widening of the state and a receding of the migrant into a rugged 22 and remote isolation. Until now, the defining of the borderlands as a State of Exception (Doty, 2007) has 23 been theoretical and qualitative. This paper doesn’t retract from that but rather adds quantitative data and 24 25 interpretation to theory, making it a needed clarification of biopolitics in a time of growing use of 26 militarization at the U.S.-Mexico border and worldwide. 27 28 29 Background 30 Hot-Spot Analysis 31 Until now, Hot-Spot analysis has mostly been used for the purposes of criminology and law enforcement 32 (Levine, 2004, Eck, et al. 2005, Wortley and Townsley, 2016), mapping traffic dangers (Erdogan et al., 33 34 2008; Prasannakumar et al. 2011) and spatial epidemiology (Liu et al., 2012; Thanh Toan et al., 2013), 35 documenting where ‘crime’ is most common by records of enforcement and where disease risk is most 36 prominent by medical reports. The use of Hot-Spots in crime mapping has received critique for it not 37 38 accounting for bias and reinforcement of ‘broken windows policing’ and racism (Howell, 2009, Kindynis, 39 2014) in places known for their exclusion and control by the state (Herbert, 2008). In the case of border 40 enforcement, law enforcement officers have been known to collaborate with other law enforcement 41 agencies in data-sharing and analysis (Atabakhsh et al. 2004). The RAND Corporation has also advised 42 43 CBP and the Border Patrol on how best to use Hot-Spot analysis in intercepting and incarcerating 44 undocumented migrants (Keefe and Sullivan, 2011). In fact, a NASA funded research project advised the 45 Border Patrol to use a combination of high-resolution remote sensing and Hot-Spot analysis in order to 46 47 identify routes taken in the borderlands (Cao et al., 2007). Unlike kernel density (Soto and Martínez, 48 2018; Rossmo et al., 2008; Giordano and Spradley, 2017), Hot-Spot analysis relies less on predictive 49 measures but on the statistically significant and known spatial relationships, excluding outliers (Getis and 50 Ord, 1992). The National Institute of Justice described a Hot-Spot in criminology terms as “an area where 51 52 people have a higher than average risk of victimization” (Eck, et al., 2005). In the case of the state, such 53 reasoning may lead law-enforcement to a biased map of where they suspect violence (Wortley and 54 Mazerolle, 2008) but I argue it can be ‘flipped’ and demonstrate that it can also reveal bias, showing the 55 56 effects of enforcement by the othering that is made through a State of Exception, “the physical 57 elimination…of entire categories of citizens who for some reason cannot be integrated into the political 58 system” (Agamben, 2005, p. 2). In this case, it is the migrant, defined as a non-citizen by the state and 59 60 61 62 1 63 64 65 1 2 3 4 excluded from the political rights of society and citizenship. I argue that the Hot-Spots map this “physical 5 elimination.” 6 7 8 State of exception in Border ‘Deterrence’ 9 With the reorganizing of border enforcement in the late 20th century, undocumented migrants have gone 10 11 from a given to an exception. Where there was once a circular labor migration across the U.S.-Mexico 12 border, there now is a militarized apparatus (Dunn, 1996; Falcon, 2001; Coleman, 2005; Miller, 2014; 13 Jones, 2016; Slack et al., 2016) which funnels undocumented migrants into rugged remote territories 14 (Rubio-Goldsmith et al., 2006). This moving of routes into remote areas first became noticeable after 15 16 President Bill Clinton’s Operation Gatekeeper which sought to control illegal border crossings through a 17 local border militarization in Southern California (Nevins, 2001). Scholars have argued that the attempts 18 to control movement was in response to NAFTA, the likely crash of the Mexican agriculture economy 19 20 (Nevins, 2001; Massey, 2002), i.e., the state’s desire to control reserve labor (Marx, 1867). Even 21 government officials had suggested increased migration as a likely effect of the trade agreement before 22 implementation (Stern, 1993). In combination, were the continued migration of Central American 23 refugees resulting from the aftermaths of 1980s Reagan-era warring, like those of today fleeing violence 24 25 after Clinton’s deportation of powerful gang members to the same Central American nations (Hing, 2018) 26 and the U.S. sponsored coup in Honduras (Hamlin, 2012). While in hindsight, the infrastructures of 27 Operation Gatekeeper and the beginnings of ‘Prevention Through Deterrence’ showed an intensification 28 29 of excepting migrants, this ‘funnel effect’ has been most apparent with growth in its strategy post 9/11 30 with a ‘hardening’ border, through the expansion of traffic-stops, surveillance, walls, and a growing 31 number of agents (De Genova, 2009; Miller and Nevins, 2017), effectively creating spaces of exception 32 (Dejanovic, 2004). With the Real ID Act of 2005, the Department of Homeland Security could otherize 33 34 through these policies and places (Garrett, 2010), making the migrant not only marginalized but, in 35 theory, neutralized. The act served as a way to better control citizenship with a portrayal of social’s ills a 36 result of immigration, whether it was safety from terrorism or economic hardship (Pope & Garrett, 2012). 37 38 The George W. Bush administration had constructed policy with which the exclusion of belonging and 39 lack of rights without the proper ID exemplified Agamben’s theory of Bare Life (1998). With the US in a 40 state of constant emergency (Kaplan, 2003), the state could better define and divide based on “illegality” 41 (De Genova, 2005). 42 43 44 Although officially established for the stopping and searching of vehicles (GAO, 2009), the presence of 45 Border Patrol interior checkpoints is known to reroute migrants in some form. A shift in migrant routes 46 47 was documented after the establishment of a checkpoint on Interstate-19 south of Tucson in the 2000s 48 which moved migrants into more remote areas to the west on the Tohono O’odham reservation (Martínez 49 et al., 2014). The implementation of the Secure Border Initiative in the 2000s has also shown a significant 50 correlation into terrain with more physiological costs on the perimeters of the Altar Valley in Arizona 51 52 (Chambers, et al., 2019), a kind of local funnel effect. Central American migrants began to die in large 53 numbers in south Texas as avoiding a checkpoint lead them into “the desolate ranches and labyrinths of 54 mesquite brush that parallel the highway” (Miroff 2013). With the checkpoints’ clustering of agents, and 55 56 their surveillance and sensory technologies, migrants move further and further away, i.e. the funnel effect 57 (Rubio-Goldsmith, et al., 2006). A spatial analysis of migrant deaths in Pima County, Arizona and Brooks 58 County, Texas has documented a shift in local and regional routes (Soto and Martínez, 2018) as if the 59 Border Patrol slowly chased the excluded past the “vanishing point” created by an axis of checkpoints and 60 61 62 2 63 64 65 1 2 3 4 enforcers (Jusionyte, 2018, p. 51) and into Bare Life, a “negative territory” of both space and life (Acosta, 5 2012). Migrants are forced into places where they yield little power besides the attempts to survive and 6 7 hopefully finish their journeys, physically removed by those with power. Much has been done to 8 document this biopolitical phenomenon in concept (Amore, 2006; Doty, 2011; Dines et al., 2015) and 9 history (Sundberg 2015; Boyce, 2016), but little in the manner of mapping it out as a local/micro-spatial 10 11 condition. Soto and Martinez (2018) and Chambers (et al., 2019) documented local and regional shifts 12 and increasing remoteness in routes but also noted the need for identifying causal mechanisms. Doty 13 (2011) had pointed out that the US border enforcement worked more like a management of local space by 14 authority. Like Foucalt’s security apparatus, it allows for events to happen rather than be implemented by 15 16 direct authority (2009, p.45). I seek to show the materialization of the funnel effect and state of exception 17 by a mapped and documented example through a spatial analysis of migrant mortalities, providing 18 statistics to theory. 19 20 21 Methods 22 To map the influence of border enforcement by time and document the forming of a state of exception, I 23 sought to find whether and where migrants had taken other more-remote routes in relation to the presence 24 25 of agents in an area, i.e. the checkpoint on Arizona State Route 85 running north-south from the Mexico 26 border to Ajo, Arizona. Such remoteness could demonstrate an excluding of the people crossing. I chose 27 the Ajo corridor because of 1) its defined location of a single form of enforcement and 2) its emphasis as 28 29 an area of concern by a humanitarian group (No More Deaths, 2017). There are obviously other factors 30 such as surveillance technology (Chambers, et al., 2019) and patrolling near the border but my analysis 31 can show any patterns around the checkpoint which is placed well outside the range (Boyce, 2016) of the 32 SBInet tower locations (U.S. Customs & Border Protection, 2019) to its south and west. Apprehensions in 33 34 the Ajo area have increased in recent years, particularly by families and children (U.S. Customs & Border 35 Protection, 2018), but counts specific to the corridor were not available. Apprehensions are recorded by 36 sector and although Ajo is in the Tucson Sector, the corridor is only 25 kilometers from the Yuma Sector, 37 38 making it difficult to statistically ‘untangle.’ There is also the issue of the number of recovered remains 39 having a possible bias for the areas nearer the road because of the ease of other parties reaching this area. 40 Still, this study focuses on location. Being an analysis of the spatial influence of the checkpoint does 41 make it suitable to infer and extrapolate to other areas for additional more complex analyses. To test the 42 43 theory, I needed to determine if spatial patterns of migrant locations, in this case recorded mortalities, 44 clustered in different regions from year to year, especially before and after establishing the checkpoint. In 45 other words, I had to use a statistical analysis of both time and space of the mortalities as they related to 46 47 themselves in clusters and compare to the location of the checkpoint. Mortality data was provided by the 48 Organization Humane Borders and the Pima County Office of Medical Examiner (Arizona OpenGIS for 49 Deceased Migrants, 2018), and were used as a stand-in for migrant locations overall because the locations 50 of human remains typically show the same pattern of general routes of travel (Chamblee, et al., 2006). In 51 52 order to identify the spatial clusters of migrant mortalities in relation to time, I calculated the Getis-Ord 53 Gi* statistic, better known as Hot-Spot analysis (Getis and Ord 1992) in order to show where in the 54 landscape mortalities were clustered and where there were common locations for migrants’ remains to be 55 56 found. The clusters served as a verification of where migrant commonly traveled. The method returned z- 57 scores, which showed whether neighboring mortality locations were significantly correlated by the year 58 the bodies were found, as calculated by 59 60 61 62 3 63 64 65 1 2 3 4 푛 푛 ∗ ∑푗=1 푤푖,푗푥푗−X̄ ∑푗=1 푤푖,푗 5 퐺푖 = 2푛 푛 2 [푛 ∑ 푤 −(∑ 푤푖,푗) ] 6 푆√ 푗=1 푗−1 푗=1 7 푛−1 8 9 푛 ∑푗=1 푥푗 10 with X̄ = 푛 11 12 13 푛 ∑푛 푥2 and 푆 = √ 푗=1 푗 − (X)̄ 2 14 푛 15 16 17 where xj was the recorded year of mortality, j was the location of the mortality, wi,j was a measure of 18 significance based on the distance between mortalities, and n was the number of mortalities. 19 20 The Getis-Ord Gi* methodology maps each location as a ‘Cold’ or ‘Hot-Spot’ or whether it was 21 22 statistically significant by levels of confidence, 90%, 95%, and 99%. The Spots in the case of this study 23 represent clusters of mortalities from 2001 to the beginning of my analysis in September 2018. Hot-Spots 24 represent more recent clusters and Cold, earlier. There is a chance of error as dates of death are uncertain, 25 26 especially in the case of skeletal remains, but skeletal exposure is typically within 4 to 6 months and the 27 breaking-down of the bones in 9 months (Galloway et al., 1989). The decay timing gives a justifiable 28 assumption to substitute year found for year of death, especially in the case of clustering in certain areas. 29 It is also certain that there are uncounted deaths (Eschbach et al., 1999; Ortega, 2018) but my analysis 30 31 would not be dependent on the uncounted as I only need to compare if there is difference from near the 32 checkpoint and away from it, not whether there were unknown clusters in even more remote terrain. 33 34 35 In order to determine how far these clusters were from the checkpoint over time and whether this was 36 especially the case post-2005, when the checkpoint was established (USGAO, 2005), I conducted two 37 Analyses of Variance (ANOVA) tests: 1) Comparing groups of Hot and Cold-Spots by distance from 38 Border Patrol checkpoint, to verify if the more recent mortalities were further from the checkpoint 39 40 location, i.e., migrants shifted routes after the checkpoint was established, and 2) Comparing the 73 41 documented mortality locations before and 143 documented after the establishment of the checkpoint in 42 2005 by distances from checkpoint, so as to verify if this shift away from the area related to the 43 44 checkpoint itself. These comparisons would show in what terrain migrants were found before and after 45 the checkpoint and how separated these locations were from the societal spaces of roads and 46 communities. In essence, the Hot-Spots could show whether migrants were in spaces of exception where 47 a life would be more dependent on simple survival. 48 49 50 Results 51 52 Both ANOVA results (Tables 1 and 2) show very significant differences between the mean values with 53 54 higher distance values for more recent mortality locations, especially after the establishment of the 55 checkpoint. This means that mortalities have shifted away from the Border Patrol presence into new 56 clusters. Migrants appear to be taking more remote routes in order to avoid interception. The tests of 57 58 between groups of Hot and Cold-Spots by distance from Border Patrol checkpoint show, as seen in Figure 59 1, that the locations of more recent mortalities are typically further away from the checkpoint while in the 60 past, mortalities were nearer the roads that now have a checkpoint. Figure 2 shows how the estimated 61 62 4 63 64 65 1 2 3 4 time of mortality is related to the establishing of the checkpoint. Means and ranges are far more distant in 5 more recent times, especially after the establishment of the checkpoint. Distances shifted after the 6 7 checkpoint was established, suggesting that not only were migrant routes shifting over time but a 8 conscious decision was made to avoid the known presence of Border Patrol agents. 9 10 11 12 13 Source of 14 15 Variation SS df MS F P-value F crit 16 Between Groups 22849.7 5 4569.947 24.2 4.95E-17 2.3 17 Within Groups 24009.2 127 189 18 19 20 Total 46859 132 21 22 23 Table 1: Analysis of Variance between groups of Hot and Cold-Spots by distance from Border Patrol 24 checkpoint 25 26 27 28 Source of 29 Variation SS df MS F P-value F crit 30 31 Between Groups 4979.2 1 4979.2 15.6 0.0001 3.9 32 Within Groups 41879.8 131 319.7 33 34 35 Total 46859 132 36 37 Table 2: Analysis of Variance between pre- and post-2005 distances from Border Patrol checkpoint 38 39 40 41 Figure 1: Box and whisker plot comparing groups of Hot and Cold-Spots by distance from Border Patrol 42 checkpoint. From left to right: (1) Cold-Spot - 99% Confidence, (2) Cold-Spot - 95% Confidence, (3) 43 Cold-Spot - 90% Confidence, (4) Hold Spot - 90% Confidence, (5) Hot-Spot - 95% Confidence, and (5) 44 45 Hot-Spot - 99% Confidence 46 47 48 Figure 2: Box and whisker plot comparing distance from checkpoint by pre- (left) and post-2005 (right) 49 mortalities 50 51 52 A map of these Hot and Cold-Spots, Figure 3, shows the mortalities as points in recognizable clusters 53 from earlier recorded locations to more-recent locations. Mapping by the establishment of the checkpoint, 54 Figure 4 also shows a tendency of migrants to travel to the roads, in order to receive transportation into 55 communities (Lawrence and Widgen, 2012), rather than the remote areas before 2005. 56 57 58 Figure 3: Hot and Cold-Spots showing more recent and earlier mortalities in the Ajo valley 59 60 61 62 5 63 64 65 1 2 3 4 Figure 4: Mortalities in relation to the establishment of the checkpoint 5 6 7 In order to clarify that these remote regions were places a migrant would find more need to simply 8 survive, I inspected the locations of these Hot-Spots and what such terrain would mean for their 9 physiological condition. Results are consistent with the effects of enforced borders (Rubio-Goldsmith et 10 11 al., 2006; Rietveld, 2012; Martinez, et al., 2013) but also give detail to a local scale. The most southern 12 and recent mortalities stretch from the edges of farmland at the border in the Sonoyta Valley, across 13 canyons and washes to the Ajo Mountain Range. The most prominent points lie within the mouths and 14 passes of canyons at the base of the mountains, avoiding wider valleys and major roads, likely in attempt 15 16 to avoid detection (Dejanovic, 2004; Chávez, 2011) and possibly evidence of the influence of not only the 17 checkpoint but also SBInet surveillance and general patrolling of roads It is unlikely that the impact is 18 strictly of large surveillance as the Cold- and Hot-Spot contradictions moved specifically away from the 19 20 checkpoint towards the range of SBInet towers and yet also in more ‘hidden’ locations. This ‘hiding’ 21 suggests a compounding variables that will need further study. Similar terrain is found in the hotspots of 22 the North where migrants were in the Souceda and Sand Tank Mountains, near but before reaching 23 Interstate Highway 8 after trying to reach past the checkpoint. When migrants are pushed into more 24 25 remote areas that it is more costly in energy expenditure (Chambers, et al., 2019) and risking an extreme 26 state of exhaustion and mortality (Chamblee, et al., 2006; Rubio-Goldsmith, 2006;). The Hot and Cold- 27 Spots between areas demonstrate the separation over time as migrants were diverted away from the 28 29 checkpoint. As time passed, a prominent division formed between new and old routes where the more- 30 recent migrants have been in more rugged terrain and further from the checkpoint, highways, and the 31 town of Why, Arizona as shown in the Figures. Migrants found themselves at the base of the steep 32 Growler Mountains, pushed up against a ‘wall’ of rock in order to avoid the Border Patrol, as shown in 33 34 the difference in terrain between Hot- and Cold-Spots in Figure 5. 35 36 Figure 5: Samples showing typical terrain of Hot- and Cold-Spots in the Ajo valley with more recent 37 38 locations being in mountainous terrain and prior locations in valleys and nearer roads 39 40 Discussion and Conclusion 41 Agamben described life in the terms of bios, how a person lives, and zoē, biological existence and 42 43 survival (1998). He theorized that the state controls this bios, i.e., Bare Life. In the case of concentration 44 camps, a threshold was met where life was structured in a space for the state to fully control and make 45 Bare Life people’s state of existence, separated from a life of ‘living.’ In that instance, this was conceived 46 47 and operated in a pseudo-science by race (Giaccaria and Minca, 2011). Sofsky (1999) emphasized how 48 the forcing of Bare Life was dependent on both isolation and limiting of spaces, which has been suggested 49 as an aspect of migrant detention centers (Mountz, et al., 2013) and refugee camps (Rajaram and Grundy- 50 Warr, 2004; Chambers, et al., 2018) and hinted at for the Sonoran desert (Dejanovic, 2004; Chamblee, et 51 52 al., 2006: 23) but this study shows a defined space where migrants are excluded by the state. In the case 53 of the rugged terrain of the Sonoran Desert, migrants are separated from society by their state-defined 54 citizenship, overwhelmingly connected to race, place of origin and social class (Rodríguez, 1996; 55 56 Romero, 2008; Doty, 2011; Jones, 2016)In order to control “reserve labor” (Marx, 1867) and the 57 composition of race in a colonial state (Sundberg, 2015), the checkpoint can functions as a both a 58 “material construct” (Davis, 2016, p. 108) and a mechanism of carcerality without built enclosure. Border 59 Patrol restricts the accessible space of migrants by forcing them into remote, less-easily traversable spots 60 61 62 6 63 64 65 1 2 3 4 (Boyce et al., 2019), excluding them from places even without built walls (Rubio-Goldsmith, et al., 2006; 5 Burridge, 2009; Chambers, et al., 2019). These spaces are ultimately concentrated and constrained. 6 7 Badiou described a world “artificially kept separate from general humanity” (2008: 60) and Gregory 8 (2006). My study shows how borders in general and the Ajo corridor in specific function as such a world 9 — a “zone of indistinction between the law and its suspension.” 10 11 12 Where an agency may use Hot-Spot analysis to map the ‘crime’ of border crossing, my work has 13 repurposed the knowledge to draw what policy has done. My analyses show that not only have migrants 14 shifted their routes further from the U.S. Border Patrol’s checkpoint in the Ajo valley corridor, but that 15 16 migrants are now finding themselves in remote mountains and canyons away from known Border Patrol 17 presence. The Hot-Spots of recent mortalities shows migrants going around this presence of the state by 18 following the periphery of the valley. The Border Patrol has effectively pushed migrants not so much in 19 20 deterring them but in isolating them. Migrants are excluded by the state to localities where they can no 21 longer be part of the global world. The exclusion of the checkpoint is a part of a larger more expanding 22 border which “move national borders both outward, beyond sovereign territory, and inward, away from 23 official checkpoints” and “affixed to migrant’s bodies (Mountz, et al., 2013, p. 532). A of exception 24 25 becomes evident in the Cold-Spot, the place migrants once were and the Hot-Spot, where they now hide. 26 Before, migrants would have been found at more traditional crossing points with chance of survival but 27 history and my research show that the growth of the ‘prevention through deterrence’ tactic has allowed 28 29 the state’s power to balloon and the migrant’s to constrict, excluding the migrant to Bare Life, 30 geographically separated from the regular spaces of society in the U.S.. Before social death (Cacho, 2012) 31 in their final destinations, before detention, and before deportation, the state looks to force migrants to 32 hide themselves, isolated by vast desert and abrupt mountains. Some may escape but likely still be 33 34 diverted into another sort of isolation after the success of reaching their destination. In such a case, these 35 remote spaces of exception may help demonstrate the blurry line between bio and necropolitics (Mbembé 36 and Meintjes, 2003). 37 38 39 40 Still, this ‘hiding’ or use of exception in order to avoid the state could demonstrate with consideration of 41 successful crossings attempts to refuse bare life (Doty, 2011). For this reason, I recognize an opportunity 42 43 for a more in-depth comparative analysis of the hot-spots of exception to other data sources such as 44 remnants of crossing other than mortalities and anonymous social surveys of successful crossers. I believe 45 there is also a need for further analysis to map the interactions between multiple forms of coinciding 46 47 enforcement like that of the surveillance and patrolling. The nature of the practices that create bare life 48 and the risks of assisting these practices limit options but my study shows opportunity for re-inventing the 49 tools of mapping criminality to serve the excepted (Walsh, 2013). What such research of Hot-Spots can 50 do is take theory of borders and biopolitics out of a strict abstract sense and into maps of their 51 52 materialization. The Hot-Spots can serve as a documentation of the political and material realities of 53 border policy. 54 55 56 Citations 57 Acosta, A., 2012. Hinging on Exclusion and Exception: Bare Life, the US/Mexico Border, and Los que 58 nunca llegarán. Social Text, 30(4 (113)), pp.103-123. 59 60 61 62 7 63 64 65 1 2 3 4 Agamben, G., 1998. Homo sacer: Sovereign power and Bare Life. Stanford University Press. 5 6 7 Amoore, L., 2006. Biometric borders: Governing mobilities in the war on terror. Political geography, 8 25(3), pp.336-351. 9 10 11 Arizona OpenGIS for Deceased Migrants. Retrieved October 1, 2018, from http://humaneborders.info/ 12 13 Atabakhsh, H., Larson, C., Petersen, T., Violette, C. and Chen, H., 2004, June. Information sharing and 14 collaboration policies within government agencies. In International Conference on Intelligence and 15 16 Security Informatics (pp. 467-475). Springer, Berlin, Heidelberg. 17 18 Badiou, A., 2008. The meaning of Sarkozy (p. 100). London: Verso. 19 20 21 Boyce, G.A., 2016. The rugged border: Surveillance, policing and the dynamic materiality of the 22 US/Mexico frontier. Environment and Planning D: Society and Space, 34(2), pp.245-262. 23 24 25 Boyce, G.A., Chambers, S.N., Launius, S.A. 2019. Bodily Inertia and the Weaponization of the Sonoran 26 Desert in United States Boundary Enforcement. Journal on Migration and Human Security. 27 28 29 Burridge, A., 2009. Differential criminalization under operation streamline: Challenges to freedom of 30 movement and humanitarian aid provision in the Mexico-US borderlands. Refuge: Canada's Journal on 31 Refugees, 26(2). 32 33 34 Cacho, L.M., 2012. Social death: Racialized rightlessness and the criminalization of the unprotected. 35 NYU Press. 36 37 38 Cao, L., Stow, D., Kaiser, J. and Coulter, L., 2007. Monitoring cross-border trails using airborne digital 39 multispectral imagery and interactive image analysis techniques. Geocarto International, 22(2), pp.107- 40 125. 41 42 43 Chamblee, J.F., Christopherson, G.L., Townley, M., DeBorde, D. and Hoover, R., 2006. Mapping 44 migrant deaths in southern Arizona: The Humane Borders GIS. Unpublished report. 45 46 47 Chambers, S.N., Boyce, G.A., Launius, S. and Dinsmore, A., 2019. Mortality, Surveillance and the 48 Tertiary “Funnel Effect” on the US-Mexico Border: A Geospatial Modeling of the Geography of 49 Deterrence. Journal of Borderlands Studies, pp.1-26. 50 51 52 Chávez, S., 2011. Navigating the US-Mexico border: the crossing strategies of undocumented workers in 53 Tijuana, Mexico. Ethnic and Racial Studies, 34(8), pp.1320-1337. 54 55 56 Coleman, M., 2005. US statecraft and the US–Mexico border as security/economy nexus. Political 57 Geography, 24(2), pp.185-209. 58 59 60 61 62 8 63 64 65 1 2 3 4 De Genova, N., 2005. Working the boundaries: Race, space, and “illegality” in Mexican 5 6 Chicago. Duke University Press. 7 8 De Genova, N., 2009. Conflicts of mobility, and the mobility of conflict: Rightlessness, presence, 9 subjectivity, freedom. Subjectivity, 29(1), pp.445-466. 10 11 12 Dejanovic, S., 2004. Invisible Bodies, Illusionary Securing: The Performance of Illegality at the US- 13 Mexico Border. Violent Interventions, 2, p.81. 14 15 16 Dines, N., Montagna, N. and Ruggiero, V., 2015. Thinking Lampedusa: border construction, the spectacle 17 of Bare Life and the productivity of migrants. Ethnic and Racial Studies, 38(3), pp.430-445. 18 19 20 21 Doty, R.L., 2007. States of exception on the Mexico–US border: Security,“decisions,” and civilian border 22 patrols. International political sociology, 1(2), pp.113-137. 23 24 25 Doty, R.L., 2011. Bare Life: border-crossing deaths and spaces of moral alibi. Environment and Planning 26 D: Society and Space, 29(4), pp.599-612. 27 28 Dunn, T.J., 1996. Militarization of the United States-Mexico Border, 1978-1992. University of Texas 29 30 Press. 31 32 Eck, J., Chainey, S., Cameron, J. and Wilson, R., 2005. Mapping crime: Understanding hotspots. 33 34 35 Eschbach, K., Hagan, J., Rodriguez, N., Hernandez-Leon, R. and Bailey, S., 1999. Death at the Border. 36 International Migration Review, 33(2), pp.430-454. 37 38 39 Falcon, S., 2001. Rape as a weapon of war: Advancing human rights for women at the US-Mexico border. 40 Social Justice, 28(2 (84), pp.31-50. 41 42 43 Foucault, M., 2009. Securitate, teritoriu, populatie. Cluj: Editura Idea Design & Print. 44 45 Galloway, A., Birkby, W.H., Jones, A.M., Henry, T.E. and Parks, B.O., 1989. Decay rates of human 46 remains in an arid environment. Journal of Forensic Science, 34(3), pp.607-616. 47 48 49 Garrett, T.M., 2010. The border fence, immigration policy, and the Obama administration: a cautionary 50 note. Administrative Theory & Praxis, 32(1), pp.129-133. 51 52 53 Getis, A. and Ord, J.K., 1992. The analysis of spatial association by use of distance statistics. 54 Geographical analysis, 24(3), pp.189-206. 55 56 Giaccaria, P. and Minca, C., 2011. Topographies/topologies of the camp: Auschwitz as a spatial 57 58 threshold. Political Geography, 30(1), pp.3-12. 59 60 61 62 9 63 64 65 1 2 3 4 Giordano, A. and Spradley, M.K., 2017. Migrant deaths at the Arizona–Mexico border: Spatial trends of a 5 mass disaster. Forensic science international, 280, pp.200-212. 6 7 8 Government Accountability Office (GAO), 2009. Border Patrol: Checkpoints Contribute to Border 9 Patrol’s Mission, But More Consistent Data Collection and Performance Measurement Could Improve 10 11 Effectiveness. 12 13 Gregory, D., 2006. The black flag: Guantánamo Bay and the space of exception. Geografiska Annaler: 14 Series B, Human Geography, 88(4), pp.405-427. 15 16 17 Hamlin, R., 2012. Illegal refugees: Competing policy ideas and the rise of the regime of deterrence in 18 American asylum politics. Refugee Survey Quarterly, 31(2), pp.33-53. 19 20 21 Humane Borders, 2005, “The Story of Humane Borders”, 22 http://www.humaneborders.org/news/news_storymarch05.html 23 24 Herbert, S., 2008. Contemporary geographies of exclusion I: traversing Skid Road. Progress in Human 25 26 Geography, 32(5), pp.659-666. 27 28 Hing, B.O., 2018. American Presidents, Deportations, and Human Rights Violations: From Carter to 29 30 Trump. Cambridge University Press. 31 32 Howell, K.B., 2009. Broken lives from broken windows: The hidden costs of aggressive order- 33 maintenance policing. NYU Rev. L. & Soc. Change, 33, p.271. 34 35 36 Jones, R., 2016. Violent borders: Refugees and the right to move. Verso Books. 37 38 39 Jusionyte, I., 2018. Threshold: Emergency Responders on the US-Mexico Border (Vol. 41). Univ of 40 California Press. 41 42 Keefe, R. and Sullivan, T., 2011. Resource-constrained spatial Hot-Spot identification. RAND CORP 43 44 ARLINGTON VA NATIONAL SECURITY RESEARCH DIV. 45 46 Kindynis, T., 2014. Ripping up the map: Criminology and cartography reconsidered. British journal of 47 48 criminology, 54(2), pp.222-243. 49 50 Lawrence, S. and Wildgen, J., 2012. Manifold Destiny: Migrant deaths and destinations in the Arizona 51 desert. Growth and Change, 43(3), pp.482-504. 52 53 54 Levine, N., 2004. CrimeStat III: a spatial statistics program for the analysis of crime incident locations 55 (version 3.0). Houston (TX): Ned Levine & Associates/Washington, DC: National Institute of Justice. 56 57 58 59 60 61 62 10 63 64 65 1 2 3 4 Liu, Y., Li, X., Wang, W., Li, Z., Hou, M., He, Y., Wu, W., Wang, H., Liang, H. and Guo, X., 2012. 5 Investigation of space-time clusters and geospatial Hot-Spots for the occurrence of tuberculosis in 6 7 Beijing. The International Journal of Tuberculosis and Lung Disease, 16(4), pp.486-491. 8 9 Martinez, D., Reineke, R., Rubio-Goldsmith, R., Anderson, B., Hess, G. and Parks, B., 2013. A continued 10 11 humanitarian crisis at the border: Undocumented border crosser deaths recorded by the Pima County 12 Office of the Medical Examiner, 1990-2012. 13 14 Marx, K., 1867. Progressive Production of a Relative surplus population or Industrial Reserve Army. 15 16 Capital Volume One (Das Kapital). 17 18 Massey, D.S., Durand, J. and Malone, N.J., 2002. Beyond smoke and mirrors: Mexican immigration in an 19 20 era of economic integration. Russell Sage Foundation. 21 22 Mbembé, J.A. and Meintjes, L., 2003. Necropolitics. Public culture, 15(1), pp.11-40. 23 24 25 Miller, T., 2014. Border patrol nation: dispatches from the front lines of homeland security. City Lights 26 Publishers. 27 28 29 Miller, T. and Nevins, J., 2017. Beyond Trump’s Big, Beautiful Wall: Trump’s plan to wall off the entire 30 US-Mexico border is just one of a growing list of actions that extend US border patrol efforts far past the 31 international boundary itself. NACLA Report on the Americas, 49(2), pp.145-151. 32 33 34 Miroff, Nick. 2013. South Texas county faces wave of migrant deaths. The Washington Post. 35 http://articles.washingtonpost.com/2013-05-08/world/39114984_1_illegal-migrants-u-s-border-patrol- 36 checkpoint 37 38 39 Mountz, A., Coddington, K., Catania, R.T. and Loyd, J.M., 2013. Conceptualizing detention: Mobility, 40 containment, bordering, and exclusion. Progress in Human Geography, 37(4), pp.522-541. 41 42 43 Nevins, J., 2001. Operation Gatekeeper: The rise of the “illegal alien” and the remaking of the US– 44 Mexico boundary. Routledge. 45 46 47 No More Deaths. 2017. Preventing deaths, and helping recover remains, in the Ajo migration corridor. 48 https://webcms.pima.gov/UserFiles/Servers/Server_6/File/Government/Medical%20Examiner/Resources/ 49 Annual-Report-2016.pdf (accessed 15 May. 2019). 50 51 52 Ortega, C. 2018. Border Patrol failed to count hundreds of migrant deaths on US soil. CNN. 53 https://www.cnn.com/2018/05/14/us/border-patrol-migrant-death-count-invs/index.html (Accessed 15 54 Nov. 2018). 55 56 57 Pima County Office of the Medical Examiner. 2017. Annual Report 2017. 58 https://webcms.pima.gov/UserFiles/Servers/Server_6/File/Government/Medical%20Examiner/Resources/ 59 Annual-Report-2016.pdf (accessed 6 Feb. 2019). 60 61 62 11 63 64 65 1 2 3 4 5 6 Pope, P.J. and Garrett, T.M., 2013. America’s homo sacer: Examining US deportation hearings 7 and the criminalization of illegal immigration. Administration & Society, 45(2), pp.167-186. 8 9 Rajaram, Prem Kumar, and Carl Grundy‐ Warr. "The irregular migrant as homo sacer: Migration and 10 detention in Australia, Malaysia, and Thailand." International Migration 42, no. 1 (2004): 33-64. 11 12 13 Romero, M., 2008. Crossing the immigration and race border: A critical race theory approach to 14 immigration studies. Contemporary Justice Review, 11(1), pp.23-37. 15 16 17 Rossmo, D.K., Thurman, Q.C., Jamieson, J.D. and Egan, K., 2008. Geographic patterns and profiling of 18 illegal crossings of the southern US border. Security Journal, 21(1-2), pp.29-57. 19 20 21 Rietveld, P., 2012. Barrier Effects of Borders: Implications for Border-Crossing Infrastructures. European 22 Journal of Transport & Infrastructure Research, 12(2). 23 24 25 Slack, J., Martinez, D.E., Lee, A.E. and Whiteford, S., 2016. The geography of border militarization: 26 Violence, death and health in Mexico and the United States. Journal of Latin American Geography, 27 15(1), pp.7-32. 28 29 30 Sofsky, W., 2013. The order of terror: The concentration camp. Princeton University Press. 31 32 Soto, G. and Martínez, D.E., 2018. The geography of migrant death: implications for policy and forensic 33 34 science. In Sociopolitics of Migrant Death and Repatriation (pp. 67-82). Springer, Cham. 35 36 Stern, M., 1993. Congress Warned: NAFTA No Fix for Migrant Influx. San Diego Union-Tribune. 37 38 39 Sundberg, J., 2015. The state of exception and the Imperial Way of Life in the United States—Mexico 40 Borderlands. Environment and Planning D: Society and Space, 33(2), pp.209-228. 41 42 43 Thanh Toan, D.T., Hu, W., Quang Thai, P., Ngoc Hoat, L., Wright, P. and Martens, P., 2013. Hot-Spot 44 detection and spatio-temporal dispersion of dengue fever in Hanoi, Vietnam. Global health action, 6(1), 45 p.18632. 46 47 48 U.S. Customs & Border Protection., 2018. Large Group Apprehensions Continue Near Ajo. (2018, 49 December 10). Retrieved May 14, 2019, from https://www.cbp.gov/newsroom/local-media-release/large- 50 group-apprehensions-continue-near-ajo 51 52 53 U.S. Customs and Border Protection., 2019.SBInet Tucson West Tower Project. (2019, February 15). 54 Retrieved May 14, 2019, from https://www.cbp.gov/border-security/along-us-borders/technology- 55 innovation-acquisition/sbinet-tucson-west-tower-project 56 57 58 US Government Accountability Office, 2005. Border Patrol: Available Data on Interior Checkpoints 59 Suggest Differences in Sector Performance. 60 61 62 12 63 64 65 1 2 3 4 5 6 Walsh, J., 2013. Remapping the border: geospatial technologies and border activism. 7 Environment and Planning D: Society and Space, 31(6), pp.969-987. 8 9 Wortley, R. and Mazerolle, L., 2008. Environmental criminology and crime analysis: Situating the theory, 10 analytic approach and application. Environmental criminology and crime analysis, pp.1-18. 11 12 13 Wortley, R. and Townsley, M. eds., 2016. Environmental criminology and crime analysis (Vol. 18). 14 Taylor & Francis. 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 13 63 64 65 Figure 1 Figure 2 Figure 3 Figure 4 Figure 5